GPL-SLAM: A Laser SLAM Framework with Gaussian Process Based Extended Landmarks

TL;DR

GPL-SLAM introduces a Gaussian Process-based object landmark representation for SLAM, enabling efficient, probabilistic, and semantic mapping that improves localization accuracy and supports safe navigation in structured environments.

Contribution

The paper presents a novel GP-based landmark modeling approach within a Bayesian SLAM framework, offering online contour updates, semantic information, and confidence bounds for improved mapping.

Findings

Accurate localization in synthetic and real environments

Efficient memory usage through recursive contour updates

Provides shape confidence bounds for safe navigation

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.