用高斯过程建模物体轮廓，实现更精准的环境感知与定位
GPL-SLAM: A Laser SLAM Framework with Gaussian Process Based Extended Landmarks
- 以物体为单位，用高斯过程表示环境轮廓
- 在线递归更新，内存效率高，支持概率关联
- 提供形状置信区间，适合安全导航等下游任务
我们提出一种新型的同步定位与地图构建（SLAM）方法，采用基于高斯过程（GP）的地标（物体）表示。不同于传统的栅格地图或点云配准，该方法以物体为单位，使用基于高斯过程的轮廓表示环境，并通过递归方案在线更新，实现高效内存利用。整个SLAM问题在全贝叶斯框架下建模，支持对机器人位姿和基于物体的地图进行联合推断。该表示可提供物体数量、面积等语义信息，并支持物体与测量间的概率关联。此外，基于高斯过程的轮廓能输出物体形状的置信区间，为安全导航与探索等下游任务提供有价值的信息。我们在合成数据和真实世界实验中验证了该方法，在多种结构化环境中均表现出准确的定位与建图性能。
原文摘要 · Abstract (English)
We present a novel Simultaneous Localization and Mapping (SLAM) method that employs Gaussian Process (GP) based landmark (object) representations. Instead of conventional grid maps or point cloud registration, we model the environment on a per object basis using GP based contour representations. These contours are updated online through a recursive scheme, enabling efficient memory usage. The SLAM problem is formulated within a fully Bayesian framework, allowing joint inference over the robot pose and object based map. This representation provides semantic information such as the number of objects and their areas, while also supporting probabilistic measurement to object associations. Furthermore, the GP based contours yield confidence bounds on object shapes, offering valuable information for downstream tasks like safe navigation and exploration. We validate our method on synthetic and real world experiments, and show that it delivers accurate localization and mapping performance across diverse structured environments.
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