Project example: Real-Time Slope Detection for Safer Farming with 3D LiDAR
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Manon Cortial

In one of our latest projects, we were thrilled to work with INRAE Clermont-Auvergne-Rhone-Alpes on a critical initiative: detecting dangerous slope angles in real-time along a tractor’s future trajectory using onboard LiDAR. The goal? Prevent rollovers on steep terrain by giving the tractor’s control system early warnings about hazardous inclines.

 Why it’s important: Tractor rollovers are a major safety risk in agriculture. By anticipating slope changes ahead of the vehicle, we can help operators and automated systems avoid dangerous situations—improving both safety and operational efficiency.

 How we made it happen:

  • LiDAR + Point Cloud Analysis: We started by helping select the right LiDAR and optimize its positioning on the vehicle. Then, we processed dense 3D point clouds and IMU data to map the terrain and calculate surface normals, identifying slope angles in real time.
  • ROS2 pipeline and CAN integration: take advantage of state of the art real time technologies and ensure seamless communication with the tractor’s control unit.
  • Rapid Iteration: Thanks to close collaboration with INRAE, we quickly refined the solution to match exactly their needs and specifications.

See it in action in the video below: The tractor’s projected trajectories, the LiDAR point clouds transformed back in the environment’s frame and the slope normals colored by potential rollover risks—all in real time!

This project showcases how Perception4D’s expertise in 3D and 4D perception (3D + time) can elevate your projects in autonomous vehicles, mobile robotics, or any industrial applications using 3D sensors, (LiDAR, RGBD cameras, etc.).

Working on something similar? Let’s talk! We’d love to explore how we can collaborate to turn your 3D data into actionable solutions.

#LiDAR #ROS2 #AgriculturalRobotics #CANBus #3DPerception #SafetyInnovation #INRAE #PointCloud #AutonomousVehicles #3D #mobileRobot #RobotPerception

CT-ICP LiDAR paper finalist of ICRA “Outstanding Paper” award
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Perception 4D

We just learnt that the ICRA 2022 paper “CT-ICP: Real-Time Elastic LiDAR Odometry with Loop Closure” by Pierre Dellenbach, Jean-Emmanuel Deschaud, Bastien Jacquet, François Goulette has been selected as one of three finalists for the ICRA 2022 Outstanding paper awards!

Fig. 1: Top, in color, one LiDAR scan; the color depends on the timestamp of each point (from the oldest in blue to newestin red). The scan is deformed elastically to align with the map (white points) by the joint optimization of two poses at the start and end of the scan and interpolation according to the timestamp, hence creating a continuous-time scan-to-map odometry. Below, the formulation of our trajectory with a continuity of poses intra-scan and discontinuity between scans.

Fig. 2: Aggregated point clouds for NCLT dataset (top left), KITTI-CARLA (top-right), Newer College Dataset (bottom left), and ParisLuco (bottom right) show the quality of the maps obtained with CT-ICP.

Fig. 4: Qualitative results of loop closure on the sequence 00 of KITTI-360 (11501 scans). The top left is an elevation image built by projecting the local map. The top right shows both the CT-ICP odometry’s trajectory and the one corrected using the computed Loop Closure constraints (CT-ICP+LC). The bottom shows the different loop closure constraints (green) found for the same turn as the local map at the top left.

Contributions:
• New elastic LIDAR odometry based on the continuity of poses intra-scan and discontinuity between scans.
• Local map based on a dense point cloud stored in a sparse voxel structure to obtain real-time processing speed.
• Large campaign of experiments on 7 datasets in driving and high-frequency motion scenarios, all reproducible with public and permissive open-source code (C++ & Python bindings).
• Fast method of loop detection integrated with a pose graph back-end to build a complete SLAM, integrated into pyLiDAR-SLAM.

Congrats to Pierre Dellenbach, Jean-Emmanuel Deschaud, Bastien Jacquet, François Goulette !
(And especially the first two for the hand-on coding sessions over the summer 🙂 )

You can download the full article on arXiv and HAL.