Deep Unrolled Sparse Bayesian Learning for Occupancy Grid Mapping

Abstract

Occupancy grid mapping represents the surrounding environment with a discretized grid, providing information about obstacles and the drivable region using sensors such as LiDAR or radar. For automotive driving applications, these maps are central to safe autonomous navigation. While both model-driven and deep learning-based approaches exist, this thesis develops a hybrid method to estimate the occupancy grid map from point cloud data. Specifically, the proposed method builds on the pattern-coupled sparse Bayesian learning (PC-SBL) algorithm, which is well suited to the block-sparse, spatially correlated structure of automotive grids. By replacing explicit parameter updates with a lightweight convolutional neural network, the spatial correlations and sparsity profile are learned directly from the data. Based on qualitative and quantitative evaluation on LiDAR point cloud data from the nuScenes dataset, we show that the proposed approach surpasses the strong PC-SBL baseline in both accuracy and runtime. Moreover, when applied without further training to LiDAR and radar point clouds from the RADIATE dataset, it marginally outperforms PC-SBL, indicating robust cross-dataset and cross-sensor generalization.