CVPR 2023 - Efficient Map Sparsification Based on 2D and 3D Discretized Grids

In this episode we discuss Efficient Map Sparsification Based on 2D and 3D Discretized Grids by Xiaoyu Zhang, Yun-Hui Liu. The paper proposes an efficient linear approach for map sparsification, which involves selecting a subset of landmarks from a larger map for robot navigation. Existing methods require heavy computation and memory capacity, especially for large-scale environments. The proposed approach uses a 2D discretized grid for landmark selection and introduces a space constraint term based on 3D grids to address the impact of different spatial distributions. The experiments demonstrate that the proposed method outperforms previous methods in both efficiency and performance. Relevant codes will be released on GitHub.