Waliner: Lightweight and Resilient Plugin Mapping Method with Wall Features for Visually Challenging Indoor Environments

Abstract

Vision-based indoor navigation systems have been proposed previously for service robots. However, in real-world scenarios, many of these approaches remain vulnerable to visually challenging environments such as white walls. In-home service robots, which are mass-produced, require affordable sensors and processors. Therefore, this paper presents a lightweight and resilient plugin mapping method called Waliner, using an RGB-D sensor and an embedded processor equipped with a neural processing unit (NPU). Waliner can be easily implemented in existing algorithms and enhances the accuracy and robustness of 2D/3D mapping in visually challenging environments with minimal computational overhead by leveraging a) structural building components, such as walls; b) the Manhattan world assumption; and c) an extended Kalman filter-based pose estimation and map management technique to maintain reliable mapping performance under varying lighting and featureless conditions. As verified in various real-world in-home scenes, the proposed method yields over a 5 % improvement in mapping consistency as measured by the map similarity index (MSI) while using minimal resources.