  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).

Robotics;Computer Vision and Pattern Recognition