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.

我们提出了一种名为 DeepPointMap 的统一架构，利用神经网络从点云中提取高度代表性和稀疏的神经描述符，实现了内存高效的地图表示和准确的多尺度定位任务，并通过将其扩展到多项协作环境证明了其框架的多功能性、有效性和潜力。