CNN Based Perception System for Collision Avoidance in Mobile Robots using Stereo Vision

In this paper, a CNN based perception system for collision avoidance in mobile robots is presented. In the considered scenario, a mobile robot is ordered to reach a target on its workspace, where several types of objects influence a collision risk for a suitable movement to the desired goal. To ensure collision-free planning through the environment, a set of convolutional neural networks in parallel are employed to detect a set of static or dynamic objects of interest in the environment, as well as objects on the floor that could imply a collision risk during a movement execution. Afterward, stereo vision and filtering algorithms are employed to recover and track the spatial position of detections, in order to generate enough information to plan a collision-free trajectory. All the above steps are evaluated in real-time and real environments, proving to be enough robust and fast for a wide range of mobile robot applications.