|Abstract:||There are a number of challenges specific to autonomous urban snow removal that make localization and navigation difficult tasks. The vehicle’s position and orientation must be precisely estimated in order to efficiently plow rows of snow into designated areas. The nature of the application necessitates a proximity to buildings, limiting the reliability of GPS data. Wheel odometry and inertial navigation provide smooth, accurate location and heading estimates over small distances. However, the error in this estimate grows without bound and is sensitive to wheel slip, an inevitability when moving heavy piles of snow on ice, packed snow, or similar low friction surfaces. Active beacons, on the other hand, provide an external absolute frame of reference that is immune to drift and the effects of wheel slip, while remaining effective around buildings. However, until recently, the cost, complexity, and error associated with commercially viable offerings of this class of sensor system has typically been too large for the precision necessary in the context of urban snow removal applications. Given the complimentary advantages and disadvantages of sensors with either relative or absolute frames of reference, a combination of wheel odometry, inertial navigation, and active beacons can provide smooth local estimation while eliminating drift and the adverse effects of wheel slip. In this paper we present an autonomous robotic snowplow that localizes itself by integrating measurements from wheel encoders, an inertial measurement unit (IMU), and Ultra-Wideband (UWB) beacons using an Extended Kalman Filter (EKF). Localization estimates that include a combination of all three sensors outperform those from any smaller subset of sensors when compared to a ground truth. The improvements are most noticeable when considerable wheel slip occurs. These results lead us to conclude that a fusion of multi-modal sensors is well-suited for this and similar applications.|
Proceedings of the 29th International Technical Meeting of the Satellite Division of The Institute of Navigation (ION GNSS+ 2016)
September 12 - 16, 2016
Oregon Convention Center
|Pages:||1178 - 1183|
|Cite this article:||
Klein, Matthew A., Hart, Charles, Quinn, Roger D., "Autonomous, Precision Snow Removal in an Urban Environment, Featuring Ultra-Wideband Beacons," Proceedings of the 29th International Technical Meeting of the Satellite Division of The Institute of Navigation (ION GNSS+ 2016), Portland, Oregon, September 2016, pp. 1178-1183.
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