DeepPointMap: Advancing LiDAR SLAM with Unified Neural Descriptors
Xiaze Zhang Ziheng Ding Qi Jing Yuejie Zhang Wenchao Ding Rui Feng
Point clouds have shown significant potential in various domains, including
Simultaneous Localization and Mapping (SLAM). However, existing approaches
either rely on dense point clouds to achieve high localization accuracy or use
generalized descriptors to reduce map size. Unfortunately, these two aspects
seem to conflict with each other. To address this limitation, we propose a
unified architecture, DeepPointMap, achieving excellent preference on both
aspects. We utilize neural network to extract highly representative and sparse
neural descriptors from point clouds, enabling memory-efficient map
representation and accurate multi-scale localization tasks (e.g., odometry and
loop-closure). Moreover, we showcase the versatility of our framework by
extending it to more challenging multi-agent collaborative SLAM. The promising
results obtained in these scenarios further emphasize the effectiveness and
potential of our approach.