Nishad Kulkarni’s Post

🆕 I successfully navigated my robot in the restaurant simulation world I created using ROS2 Humble and Gazebo. The robot was controlled using teleop with a LiDAR plugin. 🦾📡 This hands-on project demonstrates the practical application of robotics in real-world scenarios, advancing my skills in teleoperation and sensor integration. #Robotics #ROS2 #Gazebo #Teleop #LiDAR #Innovation #Tech #Simulation Thanks Karthikesh J G for providing me this task!

How to Implement the A-LOAM(Lidar Odometry and Mapping)Algorithm on hashtag #reComputer Jetson Orin with 3D LiDAR? 

📢 Our paper titled "Towards Latency Efficient DRL Inference: Improving UAV Obstacle Avoidance at the Edge Through Model Compression" has been accepted for presentation at the 27th IEEE International Conference on Intelligent Transportation Systems (ITSC 2024), a premier forum for advancements in transportation systems, held in Edmonton, Canada. 🔗 Link to the paper: https://lnkd.in/dPkC4yzX 🔍 ABSTRACT: Ensuring that autonomous Unmanned Aerial Vehicles (UAVs) can effectively avoid obstacles is crucial for their safe operation. Movement decisions must be made swiftly to prevent crashes, necessitating low inference latency. When deploying deep reinforcement learning (DRL) for obstacle avoidance, placing the DRL model at the edge (e.g., on the UAV) can help reduce latency. However, even models small enough for edge deployment can still suffer from high inference latency. This paper addresses the gap in research on reducing DRL inference time for UAVs in obstacle avoidance scenarios by exploring various model compression techniques to enhance inference speed. We propose a novel approach that integrates multiple model compression techniques and applies it to a high-performing Dueling Double Deep Q-Network (D3QN) baseline model. Testing on Nvidia Jetson Orin Nano and Nvidia Jetson Nano edge devices, our combined model compression approach demonstrates a reduction in inference latency by 38.61% and 53.18%, respectively, with only a minimal decrease in success rate by 2.34% and 5%. Our study provides valuable insights into improving latency efficiency in DRL inference for UAVs, enhancing their ability to avoid obstacles more effectively. Congratulations to Patrick McEnroe for this outstanding achievement! #IEEE #ITSC2024 #IntelligentTransportationSystems #UAV #ObstacleAvoidance #DeepReinforcementLearning #ModelCompression #EdgeComputing #mllabs #netslab #UCD SFI Centre for Research Training in Machine Learning Science Foundation Ireland University College Dublin UCD School of Computer Science Network Softwarization and Security Labs (NetsLab) IEEE IEEE ITS Society with Shen Wang Madhusanka Liyanage

How to Implement the A-LOAM(Lidar Odometry and Mapping)Algorithm on #reComputer Jetson Orin with 3D LiDAR? Find a step-by-step guide through our wiki: https://lnkd.in/gG7wSwQ4 ✅ 0"01 handhelds mapping effect in real-time ✅ 0"07 shows the complete indoor mapping using a small cart ✅ 0"34 composes 3D environment mapping structure with reComputer edge device powered by NVIDIA Robotics Jetson Orin NX, RoboSense RS32 LiDAR, and #SLAMTEC robot combined with #ROS1 More practical documents 👇 Install ROS1 on Jetson Orin: https://lnkd.in/grYPPDtP Utilize RoboSense LiDAR on Jetson Orin: https://lnkd.in/gJdcGt_6 🤔Based on these resources, leave a comment to share what's your preferred application next! #lidar #3dmapping #robotics #mapping #nvidia #jetson

Want to learn how you can design algorithms for aerial autonomy and test them in 3D simulations? Interested in using generating synthetic sensor data to verify performance? Check out this video which explains how Simulink with UAV Toolbox can be used to run simulations with sensor models so you can test your aerial autonomy early without waiting for hardware. #aerialautonomy #UAVsimulation #dronesimulation #MATLAB #Simulink

📈 Boost your lidar simulation performance with RGL:  https://lnkd.in/dHWy3ihF Robotec GPU Lidar (RGL)  is an open-source GPU-accelerated library for improved lidar performance. It will help you scale up your performance and speed up your deployment. It may prove especially useful if you develop multi-lidar robotics simulation or need vast amounts of synthetic point clouds. Ready to test it? 🚀 #robotics #simulation #machinelearning

#Ultrasound is perhaps best known as the technology that enables noninvasive body scans and underwater communication and can help us park our cars. A young startup called #Sonair out of Norway wants to employ it for something else: #3Dcomputervision used in #autonomoushardware applications. Sonair’s founder and CEO Knut Sandven believes the company’s application of #ultrasoundtechnology — a groundbreaking approach that reads sound waves to detect people and objects in 3D, with minimal energy and computational requirements — can be the basis of more useful and considerably #lessexpensive solutions than today’s more standard approach using #lidar. #ultrasoundtechnology #3Dcomputervision #autonomoushardware #sensors #beamforming

Summary series of Robotics/AI development over several years Episode 4 In the previous autonomous architecture, perception, map/localization and planning are definitely the major functional blocks inside. Regarding map/localization, lidar definitely provides a better performance compared with vision/camera. However, the main stream of perception still relies on camera/vision. Since the previous sensor stack has both of them, so definitely we could leverage both of them. Map/localization and perception can utilize the fusion of lidar and camera together. We used the traditional way of fusing them together before. Now as DL technologies evolved, BEV or occupancy network performs much better and delivers a much denser scene, and they can also handle the occlusions. Herein an old record was using the traditional way for utilizing lidar and camera. In the next post, we are going to see some dedicated application using this software stack with some new features.

Product Featuresht: Turtlebot 4 🐢 Product Features: ✅Integrated OAK-D-PRO Camera ✅Integrated RPLIDAR-A1 LiDAR ✅ROS 2 Compatibility ✅Onboard Raspberry Pi 4B (4 GB) Ready to level up your robotics game? Meet Turtlebot 4, the ultimate open-source platform for creators, educators, and researchers! Whether you’re building cutting-edge AI projects or diving into autonomous navigation, Turtlebot 4 delivers with powerful onboard computing, an OAK-D camera for 3D vision, and LiDAR for laser-sharp mapping. Choose from the Lite or Standard model to match your needs, and watch your robotics ambitions take flight! #Turtlebot4 #RoboticsRevolution #OpenSourceRobot #AIProjects #AutonomousNavigation #OAKDPRO #LiDARTechnology #RaspberryPi4 #ROS2Compatible #3DVision #RoboticsForEducation #CuttingEdgeTech #MapWithLiDAR #TechInnovation #FutureOfRobotics #CreatorsCommunity #EducatorsInTech #ResearchAndDevelopment #STEMEducation #RoboticsEngineering #SmartTech #Makerspace #TurtlebotFamily #RobotDesign #EmbeddedSystems #LinuxRobotics #AIandRobotics #HobbyistRobots #DIYRobotics #ExploreTheFuture

🚀 𝑰𝒏𝒕𝒆𝒈𝒓𝒂𝒕𝒊𝒏𝒈 2𝑫 𝑳𝒊𝑫𝑨𝑹 𝒇𝒐𝒓 𝑬𝒏𝒉𝒂𝒏𝒄𝒆𝒅 𝑹𝒐𝒃𝒐𝒕 𝑵𝒂𝒗𝒊𝒈𝒂𝒕𝒊𝒐𝒏🚀 Integrating 2𝘋 𝘓𝘪𝘋𝘈𝘙 with my mobile robot for real-time imaging and environment mapping. The LiDAR enables the robot to measure distances to surrounding objects and create a 2D map of its environment. This is a critical step in improving the robot's navigation, obstacle detection, and path planning capabilities. Using ROS 2, the data from the LiDAR will help the robot make informed decisions in real time, bringing us closer to fully autonomous operation. 𝘐𝘵'𝘴 𝘧𝘢𝘴𝘤𝘪𝘯𝘢𝘵𝘪𝘯𝘨 𝘵𝘰 𝘸𝘪𝘵𝘯𝘦𝘴𝘴 𝘩𝘰𝘸 𝘴𝘦𝘯𝘴𝘰𝘳 𝘧𝘶𝘴𝘪𝘰𝘯 𝘤𝘢𝘯 𝘣𝘳𝘪𝘯𝘨 𝘴𝘮𝘢𝘳𝘵𝘦𝘳 𝘳𝘰𝘣𝘰𝘵𝘪𝘤 𝘴𝘺𝘴𝘵𝘦𝘮𝘴 𝘵𝘰 𝘭𝘪𝘧𝘦! #Robotics #LiDAR #AutonomousSystems #MobileRobots #ROS2 #SensorFusion #EnvironmentMapping #PathPlanning #Navigation #Mechatronics #RobotImaging #RealTimeData #EngineeringInnovation #TechExploration #Automation