Computationally-Efficient Sparsity-Aware Occupancy Grid Mapping for Automotive Driving

Abstract

Occupancy maps are used in automotive driving applications to understand the scene around the vehicle using sensor data like LiDAR measurements. State-of-the-art work relies on pattern-coupled sparse Bayesian learning (PCSBL) to estimate the occupancy map from LiDAR point cloud data by leveraging spatial dependencies across grids in the map. However, PCSBL has high computational complexity, posing challenges for real-time implementation on large-sized grid maps. In this work, we propose two methods to improve the computational efficiency of PCSBL for occupancy grid mapping by exploiting the narrow angular interactions of sensor measurements with the map. The first method partitions the measurements into spatially disjoint submaps that can be processed in parallel. The second method exploits the angular structure to impose a block structure on the measurement matrix, allowing more efficient matrix computations to accelerate the algorithm. Experiments on the nuScenes public dataset show that the presented methods reduce computational runtime compared to the benchmark PCSBL method while preserving detection accuracy.