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Camera and Lidar Cooperation for 3D Feature Extraction

The objective of this work is to use efficiently various sensors to create a SLAM system. This algorithm has to be fast (real-time), computationally light and efficient enough to allow the robot to navigate in the environment. Because other processes embedded require large amount of cpu-time, our objective was to use efficiently complementary sensors to obtain a fairly accurate localization with minimal computation. To reach this, we used a combination of two sensors: a 2D lidar and a camera, mounted above each other on the robot and oriented toward the same direction. The objective is to pinpoint and cross features in the camera and lidar FOV. Our optimized algorithms are based on segments detection. We decided to observe intersections between vertical lines seen with the camera and locate them in 3D with the ranges provided by the 2D lidar. First we implemented a RGB vertical line detector using RGB gradient and linking process, then a lidar data segmentation with accelerated computation and finally we used this feature detector in a Kalman filter. The final code is evaluated and validated using an advanced real-time robotic simulator and later confirmed with a