Graph-based Proprioceptive Localization Using a Discrete Heading-Length Feature Sequence Matching Approach

TL;DR

This paper introduces a graph-based proprioceptive localization method that uses sequence matching of heading-length features from robot trajectories to achieve robust localization in challenging environments without relying on external sensors.

Contribution

The paper presents a novel graph-based approach for proprioceptive localization using heading-length sequence matching, providing a low-cost fallback solution under adverse conditions.

Findings

Successfully localized in both simulated and real environments

Achieved localization accuracy within 10 meters of prior map

Operates continuously and robustly despite environmental challenges

Abstract

Proprioceptive localization refers to a new class of robot egocentric localization methods that do not rely on the perception and recognition of external landmarks. These methods are naturally immune to bad weather, poor lighting conditions, or other extreme environmental conditions that may hinder exteroceptive sensors such as a camera or a laser ranger finder. These methods depend on proprioceptive sensors such as inertial measurement units (IMUs) and/or wheel encoders. Assisted by magnetoreception, the sensors can provide a rudimentary estimation of vehicle trajectory which is used to query a prior known map to obtain location. Named as graph-based proprioceptive localization (GBPL), we provide a low cost fallback solution for localization under challenging environmental conditions. As a robot/vehicle travels, we extract a sequence of heading-length values for straight segments from the trajectory and match the sequence with a pre-processed heading-length graph (HLG) abstracted from the prior known map to localize the robot under a graph-matching approach. Using the information from HLG, our location alignment and verification module compensates for trajectory drift, wheel slip, or tire inflation level. We have implemented our algorithm and tested it in both simulated and physical experiments. The algorithm runs successfully in finding robot location continuously and achieves localization accurate at the level that the prior map allows (less than 10m).