Abstract
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.
Abstract (translated)
我们提出了一种新颖的同时定位与地图构建（SLAM）方法，该方法采用了基于高斯过程（GP）的地标（对象）表示。不同于传统的网格地图或点云配准，我们利用基于GP的轮廓表示在每个对象的基础上建模环境。这些轮廓通过递归方案在线更新，从而实现高效的内存使用。我们将SLAM问题在一个完整的贝叶斯框架中进行表述，这使得机器人姿态和基于对象的地图可以同时推断出来。这种表示不仅提供了语义信息，如对象的数量及其面积，还支持对测量与对象关联的概率评估。此外，基于GP的轮廓能够提供关于物体形状的信心界限，这对下游任务（例如安全导航和探索）非常有用。我们在合成数据集和真实世界实验中验证了该方法，并证明它能够在各种结构化环境中实现准确的定位和地图构建性能。
URL
https://arxiv.org/abs/2508.16459
