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。

DeepPointMap: 提升 LiDAR SLAM 的统一神经描述符

DeepPointMap: Advancing LiDAR SLAM with Unified Neural Descriptors

Realistic long-horizon tasks like image-goal navigation involve exploratory
and exploitative phases. Assigned with an image of the goal, an embodied agent
must explore to discover the goal, i.e., search efficiently using learned
priors. Once the goal is discovered, the agent must accurately calibrate the
last-mile of navigation to the goal. As with any robust system, switches
between exploratory goal discovery and exploitative last-mile navigation enable
better recovery from errors. Following these intuitive guide rails, we propose
SLING to improve the performance of existing image-goal navigation systems.
Entirely complementing prior methods, we focus on last-mile navigation and
leverage the underlying geometric structure of the problem with neural
descriptors. With simple but effective switches, we can easily connect SLING
with heuristic, reinforcement learning, and neural modular policies. On a
standardized image-goal navigation benchmark (Hahn et al. 2021), we improve
performance across policies, scenes, and episode complexity, raising the
state-of-the-art from 45% to 55% success rate. Beyond photorealistic
simulation, we conduct real-robot experiments in three physical scenes and find
these improvements to transfer well to real environments.

提出了一种名为 SLING 的新方法，致力于通过使用神经描述符和简单而有效的切换来改进现有的图像目标导航系统，在图像目标导航基准测试中取得了很大的成功，从而将成功率从 45％提高到 55％。