Low Complexity Dynamic Obstacle Detection for Intelligent Road Infrastructure

by Tiana Rakotovao, Paul Ménard and Carolynn Bernier (Univ. Grenoble Alpes, CEA, List, Grenoble, France)

We present a new lightweight algorithm for detecting the points related to dynamic obstacles within LIDAR point clouds. Thanks to its low complexity, the algorithm can be used either to enable near-sensor embedded functionalities or to enhance the capabilities of intelligent infrastructure in the C-ITS context. Experimental results on the real-world TUMTraf Intersection Dataset show that the proposed approach can run in real-time on an ARM Cortex A9 CPU while still reaching a detection precision of 69.1%, which is consistent with state-of-the-art performance of deep neural network-based approaches.