Cartographer_glass: 2D Graph SLAM Framework using LiDAR for Glass Environments

TL;DR

This paper introduces a method to detect and incorporate glass objects into 2D Graph SLAM using LiDAR, improving mapping accuracy in environments with transparent surfaces.

Contribution

It presents a simple, computationally inexpensive glass detection scheme and integrates it into Google Cartographer's occupancy grid for enhanced SLAM in glass-rich environments.

Findings

Improved accuracy in mapping glass environments

Effective integration of glass detection into Graph SLAM

Comparison shows advantages over particle filter-based methods

Abstract

We study algorithms for detecting and including glass objects in an optimization-based Simultaneous Localization and Mapping (SLAM) algorithm in this work. When LiDAR data is the primary exteroceptive sensory input, glass objects are not correctly registered. This occurs as the incident light primarily passes through the glass objects or reflects away from the source, resulting in inaccurate range measurements for glass surfaces. Consequently, the localization and mapping performance is impacted, thereby rendering navigation in such environments unreliable. Optimization-based SLAM solutions, which are also referred to as Graph SLAM, are widely regarded as state of the art. In this paper, we utilize a simple and computationally inexpensive glass detection scheme for detecting glass objects and present the methodology to incorporate the identified objects into the occupancy grid maintained by such an algorithm (Google Cartographer). We develop both local (submap level) and global algorithms for achieving the objective mentioned above and compare the maps produced by our method with those produced by an existing algorithm that utilizes particle filter based SLAM.