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

• URL: http://arxiv.org/abs/2005.13704v1
• Date: Wed, 27 May 2020 23:10:15 GMT
• Title: Graph-based Proprioceptive Localization Using a Discrete Heading-Length Feature Sequence Matching Approach
• Authors: Hsin-Min Cheng and Dezhen Song
• Abstract summary: Proprioceptive localization refers to a new class of robot egocentric localization methods. These methods are naturally immune to bad weather, poor lighting conditions, or other extreme environmental conditions. We provide a low cost fallback solution for localization under challenging environmental conditions.
• Score: 14.356113113268389
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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).

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