Self Supervised Occupancy Grid Learning from Sparse Radar for Autonomous Driving

Occupancy grid mapping is an important component of autonomous vehicle perception. It encapsulates information of the drivable area, road obstacles and enables safe autonomous driving. To this end, radars are becoming widely used due to their long range sensing, low cost, and robustness to severe weather conditions. Despite recent advances in deep learning technology, occupancy grid mapping from radar data is still mostly done using classical filtering approaches. In this work, we propose a data driven approach for learning an inverse sensor model used for occupancy grid mapping from clustered radar data. This task is very challenging due to data sparsity and noise characteristics of the radar sensor. The problem is formulated as a semantic segmentation task and we show how it can be learned in a self-supervised manner using lidar data for generating ground truth. We show both qualitatively and quantitatively that our learned occupancy net outperforms classic methods by a large margin using the recently released NuScenes real-world driving data.