Sensor fusion смотреть последние обновления за сегодня на .
Check out the other videos in the series: Part 2 - Fusing an Accel, Mag, and Gyro to Estimation Orientation: 🤍 Part 3 - Fusing a GPS and IMU to Estimate Pose: 🤍 Part 4 - Tracking a Single Object With an IMM Filter: 🤍 Part 5 - How to Track Multiple Objects at Once: 🤍 This video provides an overview of what sensor fusion is and how it helps in the design of autonomous systems. It also covers a few scenarios that illustrate the various ways that sensor fusion can be implemented. Sensor fusion is a critical part of localization and positioning, as well as detection and object tracking. We’ll show that sensor fusion is more than just a Kalman filter; it is a whole range of algorithms that can blend data from multiple sources to get a better estimate of the system state. Four of the main benefits of sensor fusion are to improve measurement quality, reliability, and coverage, as well as be able to estimate states that aren’t measure directly. The fact that sensor fusion has this broad appeal across completely different types of autonomous systems is what makes it an interesting and rewarding topic to learn. Check out these other references! Kalman Filter Tech Talk Series: 🤍 Get a free product trial: 🤍 Learn more about MATLAB: 🤍 Learn more about Simulink: 🤍 See what's new in MATLAB and Simulink: 🤍 © 2019 The MathWorks, Inc. MATLAB and Simulink are registered trademarks of The MathWorks, Inc. See 🤍mathworks.com/trademarks for a list of additional trademarks. Other product or brand names may be trademarks or registered trademarks of their respective holders.
The digitalization of the sensor world offers increasingly smarter sensor solutions and new sensor eco-systems, where we want to achieve more with less. Here sensor fusion is a very relevant and interesting topic to dive deeper into - utilizing more computational power and sophisticated communication capabilities to create sensor solutions that acquire multiple application information via one channel. Read more about our Danfoss Smart Sensors™: 🤍 Connect with us: Danfoss Sensing Solutions: 🤍 Twitter: 🤍 LinkedIn: 🤍 Subscribe to our channel: 🤍 Danfoss Sensing Solutions is your partner for advanced sensor technologies and application expertise. As a leading global player, we offer a comprehensive portfolio of advanced sensor technologies for monitoring and controlling fluids, pressure, and temperature. We help the industries and people we serve embrace a digital-focused future with industry-leading knowhow, world-class support, and sensors that enable a connected and sustainable future. Since 1933, Danfoss has engineered solutions that allow the world to use resources in smarter ways—driving the sustainable transformation of tomorrow. Danfoss produces more than 250,000 products in 70 factories across 25 countries every day, developing and refining solutions in response to our customers’ needs. Danfoss Sensing Solutions represents the union of application-driven sensor technologies and our commitment to helping you navigate your journey into the digital frontier.
Part 1 of sensor fusion video series showing the need for combining sensor data, for example, to estimate the attitude of an aircraft (e.g. UAV) using an inertial measurement unit (IMU). Benefits and problems of typical sensors, such as accelerometers and gyroscopes. Real-world, practical considerations and demonstrations on a real-time embedded system (STM32-based, using the C language). Future videos will cover complementary filters and extended Kalman filters. Free trial of Altium Designer: 🤍 Visit 🤍 for $2 for five 2-layer PCBs and $5 for five 4-layer PCBs. Patreon: 🤍 Git: 🤍 Serial Oscilloscope: 🤍 Euler Angles: 🤍 (from slide 17) [TIMESTAMPS] 00:00 Introduction 00:14 JLCPCB and Git Repo 00:40 Altium Designer Free Trial 01:08 Video Overview 01:44 Why Sensor Fusion? 02:23 Example: Aircraft Attitude Estimation 03:29 Euler Angles 04:27 Accelerometer 07:18 Implementation: Accelerometer Attitude Estimation 09:48 Gyroscope 11:54 Implementation: Gyroscope Attitude Estimation 13:48 Conclusions ID: QIBvbJtYjWuHiTG0uCoK
Check out the other videos in this series: Part 1 - What Is Sensor Fusion?: 🤍 Part 2 - Fusing an Accel, Mag, and Gyro to Estimation Orientation: 🤍 Part 3 - Fusing a GPS and IMU to Estimate Pose: 🤍 Part 4 - Tracking a Single Object With an IMM Filter: 🤍 Part 5 - How to Track Multiple Objects at Once: 🤍 This video describes how we can use a magnetometer, accelerometer, and a gyro to estimate an object’s orientation. The goal is to show how these sensors contribute to the solution, and to explain a few things to watch out for along the way. We’ll cover what orientation is and how we can determine orientation using an accelerometer and a magnetometer. We’ll also talk about calibrating a magnetometer for hard and soft iron sources and ways to deal with corrupting accelerations. We’ll also show a simple dead reckoning solution that uses the gyro on its own. Finally, we’ll cover the concept of blending the solutions from the three sensors. Check out these other references! Estimating Orientation Using Inertial Sensor Fusion and MPU-9250: 🤍 Kalman Filter Tech Talks: 🤍 Drone Control and the Complementary Filter: 🤍 Madgwick Filter: 🤍 Mahony Filter: 🤍 Representing Attitude: 🤍 Get a free product trial: 🤍 Learn more about MATLAB: 🤍 Learn more about Simulink: 🤍 See what's new in MATLAB and Simulink: 🤍 © 2019 The MathWorks, Inc. MATLAB and Simulink are registered trademarks of The MathWorks, Inc. See 🤍mathworks.com/trademarks for a list of additional trademarks. Other product or brand names may be trademarks or registered trademarks of their respective holders.
Deep learning-based camera perception software for ADAS and self-driving Start-up StradVision and LiDAR Technology Development Start-up VUERON Technology will showcase sensor fusion technology that combines camera and LiDAR! By integrating the 3D information collected by LiDAR sensors into the object detailed information collected through the camera sensors, the surrounding environment can be recognized more precisely to achieve a safer autonomous driving experience. Learn more about us by visiting our website 🤍
This video presents key sensor fusion strategies for combining heterogeneous sensor data in automotive SoCs. It discusses the three main fusion methods that can be applied in a perception system: early fusion, late fusion and mid-level fusion. Learn more about Synopsys: 🤍 Subscribe: 🤍 Follow Synopsys on Twitter: 🤍 Like Synopsys on Facebook: 🤍 Follow Synopsys on LinkedIn: 🤍
Check out the other videos in this series: Part 1 - What Is Sensor Fusion?: 🤍 Part 2 - Fusing an Accel, Mag, and Gyro to Estimation Orientation: 🤍 Part 3 - Fusing a GPS and IMU to Estimate Pose: 🤍 Part 4 - Tracking a Single Object With an IMM Filter: 🤍 Part 5 - How to Track Multiple Objects at Once: 🤍 This video continues our discussion on using sensor fusion for positioning and localization by showing how we can use a GPS and an IMU to estimate and object’s orientation and position. We’ll go over the structure of the algorithm and show you how the GPS and IMU both contribute to the final solution so you have a more intuitive understanding of the problem. Check out these other references! Pose Estimation From Asynchronous Sensors: 🤍 Understanding Kalman Filters: 🤍 Learn more about Kalman filters: 🤍 Get a free product Trial: 🤍 Learn more about MATLAB: 🤍 Learn more about Simulink: 🤍 See What's new in MATLAB and Simulink: 🤍 © 2019 The MathWorks, Inc. MATLAB and Simulink are registered trademarks of The MathWorks, Inc. See 🤍mathworks.com/trademarks for a list of additional trademarks. Other product or brand names may be trademarks or registered trademarks of their respective holders.
Extended Kalman Filter (EKF) overview, theory, and practical considerations. Real-world implementation on an STM32 microcontroller in C in the following video. Part 3 of sensor fusion video series. [SUPPORT] Free trial of Altium Designer: 🤍 PCBA from $0 (Free Setup, Free Stencil): 🤍 Patreon: 🤍 [LINKS] Git: 🤍 Sensor Fusion Part 2: 🤍 Sensor Fusion Part 1: 🤍 Small Unmanned Aircraft (Book): 🤍 State observers: Observers: 🤍 Euler Angles: 🤍 (from slide 17) [TIMESTAMPS] 00:00 Introduction 00:28 Previous Videos 00:41 Altium Designer Free Trial 01:05 Content 01:43 Sensor Fusion Recap 02:26 Complementary Filter Recap 03:08 Choosing alpha 03:29 Kalman Filter Overview 04:19 Estimation Error and Covariance 05:00 Non-Linear and Discrete-Time Kalman Filter 05:47 Book Recommendation 06:05 EKF Algorithm Overview 07:19 Practical Example (Attitude Estimation) 07:49 Prediction (EKF Step 1) 10:14 Update (EKF Step 2) 13:32 Complete EKF Algorithm 14:04 Practical Issues and Considerations 15:14 Next Video ID: QIBvbJtYjWuHiTG0uCoK
To achieve high levels of automated driving, we need to transition from object-based to AI-based sensor fusion. Cristina Rico, Head of Sensor Fusion at CARIAD, gives insights into our sensor fusion technology at #GTC22: 👉 🤍 #WeAreCARIAD #TimeToTransform #Mobility #SensorFusion Learn more: 🤍logy/ Follow us: Instagram 🤍 Twitter: 🤍 LinkedIn: 🤍
Check out the other videos in the series: Part 1 - What Is Sensor Fusion?: 🤍 Part 2 - Fusing an Accel, Mag, and Gyro to Estimation Orientation: 🤍 Part 3 - Fusing a GPS and IMU to Estimate Pose: 🤍 Part 4 - Tracking a Single Object With an IMM Filter: 🤍 Part 5 - How to Track Multiple Objects at Once: 🤍 Gain insights into track-level fusion, the types of tracking situations that require it, and some of the challenges associated with it. You’ll see two different tracking architectures—track-to-track fusion and central-level tracking—and learn the benefits of choosing one architecture over the other. Additional Resources: - Introduction to Track-to-Track Fusion: 🤍 - Track-to-track fusion example (MathWorks): 🤍 - Comparative Study of Track-to-Track Fusion Methods for Cooperative Tracking with Bearings-Only Measurements (PDF): 🤍 - Covariance Intersection in State Estimation of Dynamical Systems (PDF): 🤍 - Download ebook: Multi-Object Tracking for Autonomous Systems and Surveillance Systems: 🤍 - Download white paper: Sensor Fusion and Tracking for Autonomous Systems: 🤍 - Free Trial – Sensor Fusion and Tracking Toolbox: 🤍 Get a free product trial: 🤍 Learn more about MATLAB: 🤍 Learn more about Simulink: 🤍 See what's new in MATLAB and Simulink: 🤍 © 2020 The MathWorks, Inc. MATLAB and Simulink are registered trademarks of The MathWorks, Inc. See 🤍mathworks.com/trademarks for a list of additional trademarks. Other product or brand names may be trademarks or registered trademarks of their respective holders.
Extended Kalman Filter (EKF) implementation and practical considerations. Real-world, real-time implementation and demo on an STM32 microcontroller in C using accelerometer and gyroscope measurements. Part 4 (final) of sensor fusion video series. [SUPPORT] Free trial of Altium Designer: 🤍 PCBA from $0 (Free Setup, Free Stencil): 🤍 Patreon: 🤍 [LINKS] Git: 🤍 Sensor Fusion Part 3: 🤍 Sensor Fusion Part 2: 🤍 Sensor Fusion Part 1: 🤍 IIR Filters: 🤍 Tag-Connect SWD Probe: 🤍 Small Unmanned Aircraft (Book): 🤍 Euler Angles: 🤍 (from slide 17) [TIMESTAMPS] 00:00 Introduction 00:21 Altium Designer Free Trial 00:44 JLCPCB and Design Files 01:06 Pre-Requisites 01:53 'Low-Level' Firmware Overview 07:00 Axis Re-Mapping 08:17 Calibration 09:42 Filtering Raw Measurements 12:12 EKF Algorithm Overview 14:11 EKF Initialisation 17:12 EKF Predict Step 19:26 Matlab/Octave Symbolic Toolbox 21:11 EKF Update Step 22:16 Setting EKF Parameters 23:26 Debug Set-up and Tag-Connect SWD Probe 24:05 Live Demonstration 26:29 Practical Considerations ID: QIBvbJtYjWuHiTG0uCoK
Navigation is the ability to determine your location within an environment and to be able to figure out a path that will take you to a goal. This video provides an overview of how we get a robotic vehicle to do this autonomously. We’ll cover what it means to have a fully autonomous vehicle and look at the importance of mapping the environment and path planning. We’ll also show how we can accomplish full autonomy through a heuristic approach and through an optimal approach. Interested in learning more about Sensor Fusion? Send us an enquiry now! We are the sole distributor in Southeast Asia for MathWorks Inc, developer of the MATLAB® and Simulink® family of products. Get in touch with us: events🤍techsource-asia.com View Our Upcoming Events: 🤍 View Training Updates: 🤍 Don't forget to Subscribe & Follow for more Updates! Facebook: 🤍 LinkedIn : 🤍 YouTube: 🤍
Part 2 of sensor fusion video series showing theory and implementation of the complementary filter. Looking at derivation, practical issues, alternative views, and implementation on a real-world embedded system (STM32) in C. Free trial of Altium Designer: 🤍 Visit 🤍 for $2 for five 2-layer PCBs and $5 for five 4-layer PCBs. Patreon: 🤍 Git: 🤍 Serial Oscilloscope: 🤍 State observers: Observers: 🤍 Euler Angles: 🤍 (from slide 17) [TIMESTAMPS] 00:00 Introduction 00:27 Design Files/Source Code 00:47 Altium Designer Free Trial 01:30 Recap 02:08 Complementary Filter Theory 06:14 What does 'alpha' do? 06:46 Practical Considerations 07:47 Implementation (STM32) 10:08 Demonstration (Real-Time) 11:47 Alternative View: State Observer ID: QIBvbJtYjWuHiTG0uCoK
Footage shot on April 29, 2017 in Plymouth, Michigan. This Civil Maps video showcases localization in six degrees of freedom (6DoF) at high speeds using low cost sensors. Through sensor fusion, our demo vehicle is able to localize the car while driving at speeds approaching 70 mph on a major highway.
In this video of the introduction to sensor fusion for autonomous vehicles, our instructor talks about the current trends in the state of the art perception technology and what sensor fusion is. We also go over the various job roles and opportunities. Visit our Website for Job Leading Programs - 🤍 About Skill-Lync Skill-Lync helps you learn industry-relevant skills that will accelerate your career. More than 8000+ students have enrolled in courses across Mechanical, Electrical, Electronics, Civil & Computer Science engineering. We are rated 4.8/5 on Google out of 1000+ reviews. Our students now work in companies like Fiat Chrysler, Tata Motors, Ford, Ather, Mercedes Benz, Bosch, and many more. This is why you should choose Skill-Lync programs 1. Skill-Lync is an e-learning platform for Engineering students with 1000+ positive reviews on Google. 2. Learn industry-oriented technical skills. 3. Work on 15+ Industry oriented projects. Create a strong profile that will help you get recruited. 4. Have a look at the profile of a student of ours - 🤍 5. Attend live video conferencing support sessions every week. 6. Interact with industry experts and Skill-Lync support team 24/7 to get your doubts clarified 7. Get recruited with help from our placement assistance team. #SensorFusion #Skill-Lync #Workshop
Machine learning can be used to combine different sensor data together to make decisions and classifications. This is a form of sensor fusion. Instead of mixing the readings together to get something like an absolute heading (from an inertial measurement unit), we can instead feed the raw data to a neural network. The network will learn the best ways to mix the data to help make predictions and classifications. This tutorial will demonstrate the process of collecting gas data to train a machine learning model that can identify different odors. We then deploy the model to a Seeed Studio Wio Terminal so that odor classification can be performed in real time. A written guide for building this AI artificial nose can be found here: 🤍 The first part of the project involves capturing raw data from a variety of gas sensors, including temperature, humidity, pressure, equivalent CO2, NO2, ethanol, CO, and two different VOC measurements. From there, we analyze the data using Python in Google Colab. That allows us to normalize all of the data so that it fits between the range 0 and 1. Note that you will need to record the minimums and ranges for each of the sensor channels, as you will need to perform normalization on raw data during inference. Using this information, we can also drop sensor channels that do not appear to help us differentiate among odors. For example, the pressure channel offers little variation among the measurements, so we get rid of it. Next, we import our preprocessed data into an Edge Impulse project, which guides us through the process of building a neural network that can identify odors. We use Edge Impulse to test our neural network accuracy and generate an Arduino library for us to perform real-time inference. Finally, we deploy our model to the Wio Terminal, which provides us with inference results on the LCD. Product Links: Wio Terminal - 🤍 Grove - Multichannel Gas Sensor v2 - 🤍 Grove - SPG30 VOC and eCO2 Gas Sensor - 🤍 Grove - BME680 Temperature, Humidity, and Pressure Sensor - 🤍 Grove - I2C Hub - 🤍 Related Videos: Intro to TinyML Part 1: Training a Neural Network for Arduino in TensorFlow - 🤍 Intro to TinyML Part 2: Deploying a TensorFlow Lite Model to Arduino - 🤍 Related Project Links: Intro to TinyML Part 1: Training a Model for Arduino in TensorFlow - 🤍 Intro to TinyML Part 2: Deploying a TensorFlow Lite Model to Arduino - 🤍 Related Articles: What is Edge AI? Machine Learning + IoT - 🤍 Learn more: Maker.io - 🤍 Digi-Key’s Blog – TheCircuit 🤍 Connect with Digi-Key on Facebook 🤍 And follow us on Twitter 🤍
#computervision #deeplearning #selfdrivingcars A self-driving car or an autonomous vehicle needs sensors to perceive its environment and be able to make appropriate decisions. In this project, I illustrate how to fuse cameras and a LiDAR scanner and perform object detection with the help of a deep learning algorithm. The final result shows the detection of the other vehicles with their real-time distance from our vehicle. Read the full article here: 🤍 Music from Uppbeat (free for Creators!): 🤍 License code: XCFYW2D3SQNALRUF
In this IoT Central MicroSession, learn about use cases for sensor fusion and how it can be accomplished using neural networks. You will walk through how a machine learning model performing sensor fusion can be trained in Edge Impulse and tested on a microcontroller. Instructor: Shawn Hymel, Senior Developer Relations Engineer, Edge Impulse Optional Hardware: Arduino Nano 33 BLE Sense Preparation: Free sign-up at 🤍
Download the files used in this video: 🤍 Sensors are a key component of an autonomous system, helping it understand and interact with its surroundings. In this video, Roberto Valenti joins Connell D'Souza to demonstrate using Sensor Fusion and Tracking Toolbox™ to perform sensor fusion of inertial sensor data for orientation estimation. This is a common and important application for teams participating in maritime and aerial vehicle competitions. First, Connell and Roberto introduce common inertial sensors like inertial measurement units (IMU) and magnetic, angular rate, and gravity (MARG) before explaining why sensor fusion is important to make sense of this sensor data. Roberto will then use MATLAB Mobile™ to stream and log accelerometer, gyroscope, and magnetometer sensor data from his cell phone to MATLAB® and perform sensor fusion on this data to estimate orientation using only a few lines of code. The imufilter and ahrsfilter functions used in this video use Kalman filter-based fusion algorithms. The results of the fusion are compared with the orientation values streamed from the cell phone to check the accuracy of the estimation. Get a free product Trial: 🤍 Learn more about MATLAB: 🤍 Learn more about Simulink: 🤍 See What's new in MATLAB and Simulink: 🤍 © 2018 The MathWorks, Inc. MATLAB and Simulink are registered trademarks of The MathWorks, Inc. See 🤍mathworks.com/trademarks for a list of additional trademarks. Other product or brand names maybe trademarks or registered trademarks of their respective holders.
Learn to fuse lidar point clouds, radar signatures, and camera images using Kalman Filters to perceive the environment and detect and track vehicles and pedestrians over time with the Sensor Fusion Nanodegree program with Udacity.
Google Tech Talk August 2, 2010 ABSTRACT Presented by David Sachs. Gyroscopes, accelerometers, and compasses are increasingly prevalent in mainstream consumer electronics. Applications of these sensors include user interface, augmented reality, gaming, image stabilization, and navigation. This talk will demonstrate how all three sensor types work separately and in conjunction on a modified Android handset running a modified sensor API, then explain how algorithms are used to enable a multitude of applications. Application developers who wish to make sense of rotational motion must master Euler angles, rotation matrices, and quaternions. Under the hood, sensor fusion algorithms must be used in order to create responsive, accurate, and low noise descriptions of motion. Reducing sensing errors involves compensating for temperature changes, magnetic disturbances, and sharp accelerations. Some of these algorithms must run at a very high rate and with very precise timing, which makes them difficult to implement within low-power real-time operating systems. Within Android specifically, this involves modifying the sensor manager, introducing new APIs, and partitioning motion processing tasks. David Sachs began developing motion processing systems as a graduate student at the MIT Media Lab. His research there led him to InvenSense, where he continues this work with MEMS inertial sensors used in products such as the Nintendo Wii Motion Plus. David's designs incorporate gyroscopes, accelerometers, and compasses in various combinations and contexts including handset user interfaces, image stabilizers, navigation systems, game controllers, novel Braille displays, and musical instruments.
In this video we will see Sensor fusion on mobile robots using robot_localiztion package. First we will find out the need for sensor fusion, then we will see how to use robot_localization package for sensor fusion and finally will see the comparison of odometry data with and without sensor fusion. We will fuse IMU data with wheel odometry data to get more accurate robot location. I have used MPU6050 IMU sensor to get IMU data. robot_localization: 🤍
How do you distinguish between background noise and the sound of an intruder breaking glass? David Jones, head of marketing and business development for intuitive sensing solutions at Infineon, talks with Semiconductor Engineering about what types of sensors are being developed, what happens when different sensors are combined, what those sensors are being used for today, and what they will be used for in the future.
In this video I show how to use Madgwick's Filter to fuse sensors readings from an InvenSense MPU6050 gyroscope / accelerometer and a Honeywell HMC5883L magnetometer. This filter is very easy to use, with only two settings that require attention: the gain, and the sample frequency. I use Madgwick's Filter to determine the pitch angle of my balancing robot, and take the first steps toward getting the robot to balance. To help visualize the sensor fusion, I also wrote a very basic Java program using the Java3D and jSerialComm libraries. It shows a 3D cube rotating based on the quaternion output of the filter. Firmware source code is here: 🤍 The Java3D test program source code is here: 🤍 The sensor module I used is available from ICStation here: 🤍 This video is part of a series showing how to build a balancing robot: Part 1: Modify RC Servos for Continuous Rotation and External H-Bridge Control 🤍 Part 2: Building a Robot Chassis with Brass Square Tube, a Dremel, and Solder 🤍 Part 3: First Steps with a GY-86 10DOF Sensor: MPU6050, HMC5883L and MS5611 🤍 Part 4: [THIS VIDEO] 6DOF & 9DOF Sensor Fusion with Madgwick's Filter, MPU6050, HMC5883L (GY-86 Module) 🤍 Part 5: How to Use CC2500 PA LNA 2.4GHz Wireless RF Modules 🤍 Part 6: GPU-Accelerated Data Logging and Telemetry 🤍 Part 7: How to Tune PID Control Loops Visually 🤍
Full course content here: 🤍
Shawn Hymel shows how to combine sensor data using a neural network on Edge Impulse to classify the environment of various rooms in a house. In this demonstration, you will see how sensor fusion can be performed using embedded machine learning. Get started today: 🤍 Watch more ML Microsessions: 🤍
Fusion is a core attribute of the F-35, designed into the mission systems from design conception. The F-35 Sensor Fusion development leveraged experience from past fusion projects across the corporation; however, there were some fundamental architecture decisions and algorithmic challenges that were unique to the F-35 concept of operation. This presentation discusses some of the key F-35 design decisions and features that shaped the final F-35 Sensor Fusion solution.
This video will give you a brief knowledge about sensor fusion and its types used by various self driving car companies. Note that many companies prefer late fusion than early fusion as it uses black box method. Detailed explanation will be given in further videos. If you haven't watched the previous videos on autonomous vehicles, click the link given below Introduction to autonomous vehicles: 🤍 sensors used in autonomous vehicles: 🤍
IDLab and DEME successfully demonstrated a sensor fusion framework for automated docking of vessels at the DEME harbour. Multiple sensors were used to capture the precise manoeuvres within the docks generating an even better map of the environment, which can be used by the autonomous docking controllers. Communication between onboard and onshore sensors was enabled by using a LiDAR fusion framework which employed our in-house DUST framework. Watch the video below to learn more about our demo. Realised within the de blauwe cluster SSave project with partners dotOcean, Tresco, DEME, KULeuven and RMA, with the support of VLAIO.
Authors: Mario Bijelic, Tobias Gruber, Fahim Mannan, Florian Kraus, Werner Ritter, Klaus Dietmayer, Felix Heide Description: The fusion of multimodal sensor streams, such as camera, lidar, and radar measurements, plays a critical role in object detection for autonomous vehicles, which base their decision making on these inputs. While existing methods exploit redundant information in good environmental conditions, they fail in adverse weather where the sensory streams can be asymmetrically distorted. These rare edge-case'' scenarios are not represented in available datasets, and existing fusion architectures are not designed to handle them. To address this challenge we present a novel multimodal dataset acquired in over 10,000~km of driving in northern Europe. Although this dataset is the first large multimodal dataset in adverse weather, with 100k labels for lidar, camera, radar, and gated NIR sensors, it does not facilitate training as extreme weather is rare. To this end, we present a deep fusion network for robust fusion without a large corpus of labeled training data covering all asymmetric distortions. Departing from proposal-level fusion, we propose a single-shot model that adaptively fuses features, driven by measurement entropy. We validate the proposed method, trained on clean data, on our extensive validation dataset. Code and data are available here 🤍
Ryan Kastner, Multi-Sensor Fusion for Locating, Monitoring, Foraging and Breeding Behaviour of Whale Sharks Quickfire 4-minute presentations by UC San Diego faculty recipients or investigators on the 17 grants awarded for 2012-2013 by the Calit2 Strategic Research Opportunities (CSRO) program.
Check out the other videos in the series: Part 1 - What Is Sensor Fusion?: 🤍 Part 2 - Fusing an Accel, Mag, and Gyro to Estimation Orientation: 🤍 Part 3 - Fusing a GPS and IMU to Estimate Pose: 🤍 Part 4 - Tracking a Single Object With an IMM Filter: 🤍 This video describes two common problems that arise when tracking multiple objects: data association and track maintenance. We cover a few ways to solve these issues and provide a general way to approach all multi-object tracking problems. We cover data association algorithms like global nearest neighbor (GNN) and joint probabilistic data association (JPDA) and look at the criteria for deleting and creating tracks. We talk about gating observations so that we don’t waste computational resources. At the end of the video, we show an example of GNN and JPDA algorithms operating on two objects in close proximity. Check out these other references! Multi-Object Trackers: 🤍 Ebook: Multi-Object Tracking for Autonomous Systems and Surveillance Systems - 🤍 Multi-Object Tracking: 🤍 Get a free product trial: 🤍 Learn more about MATLAB: 🤍 Learn more about Simulink: 🤍 See what's new in MATLAB and Simulink: 🤍 © 2019 The MathWorks, Inc. MATLAB and Simulink are registered trademarks of The MathWorks, Inc. See 🤍mathworks.com/trademarks for a list of additional trademarks. Other product or brand names may be trademarks or registered trademarks of their respective holders.
This video demonstrates an MPC application for ADAS and automated driving systems. - Download the technical paper on adaptive cruise control design with MPC: 🤍 - What is Model Predictive Control: 🤍 - Lane Keeping Assist System Using Model Predictive Control: 🤍 - Understanding Model Predictive Control: 🤍 You’ll learn how to simulate a control system that combines sensor fusion and adaptive cruise control (ACC). Using Simulink®, you can model ACC systems with vehicle dynamics and sensors, create driving scenarios, and test the control system in a closed-loop to evaluate controller performance. You can use Automated Driving System Toolbox™ and Model Predictive Control Toolbox™ to design and simulate MPC controllers for ADAS and automated driving systems. Automated Driving System Toolbox supports multisensor fusion development and provides sensor models and scenario generation for simulating roads and surrounding cars. Using Embedded Coder®, you can automatically generate C code from your Simulink model and deploy it for software-in-the-loop (SIL) testing and hardware implementation. Get a free product Trial: 🤍 Learn more about MATLAB: 🤍 Learn more about Simulink: 🤍 See What's new in MATLAB and Simulink: 🤍 © 2018 The MathWorks, Inc. MATLAB and Simulink are registered trademarks of The MathWorks, Inc. See 🤍mathworks.com/trademarks for a list of additional trademarks. Other product or brand names maybe trademarks or registered trademarks of their respective holders.
Speaker: Fabian Girrbach Fabian is Research engineer at Xsens. In this talk, he will outline the vision and ideas for facilitating the integration of inertial motion sensors into a multi-sensor fusion setup yielding accurate state estimates of the system, providing the necessary feedback to the systems control loop. Further, Fabian will give insights into why he thinks calibrated inertial motion data should be the backbone of state estimation in indoor environments, allowing robust state estimation under all conditions. This video is a part of our ITC22 - The Future of Mobile Warehouse Robots. To watch other session of our ITC22, please go to: 🤍
Vehicles can combine data from multiple sensors (radar, camera and lidar in this case) to perform environment recognition and make decisions accordingly. Konrad Technologies presents an innovative approach to test this technology with Sensor Fusion. The approach combines ADAS sensors with Hardware-in-the-Loop style testing. Objects are simulated in real-time in a virtual environment simulating real world driving scenarios in the lab. Sensor Fusion testing enables manufacturers to proceed with confidence in developing and producing safe autonomous vehicles. Learn More Website: 🤍 Email: adas-video🤍konrad-technologies.com Automotive Sensor Test Solutions Sensor Fusion: 🤍 Radar The Konrad Technologies Vehicle Radar Test System offers users the ability to simulate and test complex automotive scenarios for validation through production. The systems are flexible, extensible and ready for software and Hardware-in-the-Loop integration. Users can combine RF measurements and custom scenarios for radar sensor functional verification with obstacle simulation capabilities. Automotive radar test systems by Konrad Technologies enable radar sensor manufactures to reduce overall development time and manufacturing cost. KT-Vehicle Radar Test System: 🤍 KT-Radar Test and Measurement Suite: 🤍 Camera The Konrad Technologies automotive camera tester allows for fully automated and reproducible tests to ensure the functionality of camera-based driver assistance systems. The automotive camera simulator test system consists of a vision system capable of stitching data from multiple cameras into a 360° image. Lidar The automotive industry aims to develop a lidar sensor for the autonomous vehicle with the perfect balance of cost, performance, reliability and size. Manufacturers can now confidently develop lidar sensors with the Konrad Technologies automotive lidar tester. The lidar technology for solid state 2D flash lidar can simulate mapping of laser sensors in the lab environment with the ability to vary distance, simulate moving objects, adjust laser intensity/distance and simulate a more/less reflective object. Ultrasonic Automotive ultrasonic sensor testing is possible with Konrad Technologies. The expertise in ADAS testing by Konrad Technologies can expand to cover the application and capabilities of testing ultrasonic technology with object simulation in the lab. Upon request, the tester can be interfaced for Hardware-in-the-Loop and Sensor Fusion. About Konrad Technologies Worldwide Konrad Technologies (KT) is a global company and NI Platinum Alliance Partner that offers customized turnkey test solutions in the areas of electronics manufacturing, high-frequency technology, optics and beyond. Since 1993, Konrad Technologies has successfully developed, designed and integrated customer-specific test solutions providing customers with R&D, qualification, and manufacturing of electronic products with tools to fulfill their quality goals, accelerate engineering and development throughput. Customers in a wide range of industries from Automotive, Aerospace and Defense, Wireless Communications, Consumer Electronics, Medical, Semiconductor, General Electronic Manufacturing to Industrial Automation use KT’s integrated hardware and software platform-based solutions to improve their performance worldwide. Konrad Technologies is a founding member of ADAS iiT, a consortium that provides a complete test solution for autonomous driving. Konrad-Technologies, KT and Konrad GmbH are all representative of Konrad Technologies worldwide. Other products and company names listed are trademarks of their respective companies. For more information about Konrad Technologies Solutions and upcoming events, please visit 🤍 #Automotive #NIWeek
The powerful #ADAS software library BASELABS Create Embedded drastically reduces the development efforts for #SensorFusion development. This demo recorded at the Vector Virtual Week shows how to generate an individual #datafusion algorithm, which is safety-compliant and is running directly on #embedded systems and ECUs with Vector's #AUTOSAR Classic software #MICROSAR in the target vehicle. More information: 🤍 🤍 Get notified when we release new videos by subscribing to our channel 🤍 and hitting the notify bell.