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.

翻译：点云在包括同时定位与地图构建（SLAM）在内的多个领域展现出显著潜力。然而，现有方法要么依赖稠密点云实现高定位精度，要么使用通用描述符减小地图规模。遗憾的是，这两个方面似乎相互冲突。为解决这一局限，我们提出统一架构DeepPointMap，在两方面均实现优异性能。我们利用神经网络从点云中提取高表征性且稀疏的神经描述符，从而兼顾内存高效的地图表示与精确的多尺度定位任务（如里程计与回环检测）。此外，我们通过将该框架扩展至更具挑战性的多智能体协同SLAM，展示了其通用性。这些场景中取得的优异成果进一步凸显了我们方法的有效性与潜力。