The LiDAR compass: Extremely lightweight heading estimation with axis maps

This paper introduces the LiDAR compass, a bounded and extremely lightweight heading estimation technique that combines a two-dimensional laser scanner and axis maps, which represent the orientations of flat surfaces in the environment. Although suitable for a variety of indoor and outdoor environme...

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Bibliographic Details
Published in:Robotics and autonomous systems 2016-08, Vol.82, p.35-45
Main Authors: Gallant, Marc J., Marshall, Joshua A.
Format: Article
Language:English
Subjects:
Compass
Heading estimation
Indoor environments
LiDAR
Lightweight
Localization
Outdoor
Position (location)
Weight reduction
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Summary:This paper introduces the LiDAR compass, a bounded and extremely lightweight heading estimation technique that combines a two-dimensional laser scanner and axis maps, which represent the orientations of flat surfaces in the environment. Although suitable for a variety of indoor and outdoor environments, the LiDAR compass is especially useful for embedded and real-time applications requiring low computational overhead. For example, when combined with a sensor that can measure translation (e.g., wheel encoders) the LiDAR compass can be used to yield accurate, lightweight, and very easily implementable localization that requires no prior mapping phase. The utility of using the LiDAR compass as part of a localization algorithm was tested on a widely-available open-source data set, an indoor environment, and a larger-scale outdoor environment. In all cases, it was shown that the growth in heading error was bounded, which significantly reduced the position error to less than 1% of the distance travelled. •A two-dimensional heading estimation algorithm called the LiDAR compass is developed.•LiDAR measurements are associated with the orientations of flat surfaces.•In many environments, the growth of heading errors is bounded.•The LiDAR compass is lightweight because it uses mostly linear operations on scalars.•When paired with odometry, a good initial estimate for graph-based SLAM is generated.
ISSN:0921-8890
1872-793X
DOI:10.1016/j.robot.2016.04.005