Autonomous Navigation On Urban Sidewalks Under Winter Conditions
2020-04
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Autonomous Navigation On Urban Sidewalks Under Winter Conditions
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2020-04
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We describe a multi-step approach to facilitate autonomous navigation in snow by small vehicles in urban environments, allowing travel only on sidewalks and paved paths. Our objective is to have a vehicle autonomously navigate from point A on one urban block to point B on another block, crossing from one block to another only at curb-cuts, and stopping when pedestrians get in the way. A small mobile platform is first manually driven along the sidewalks to continuously record LIDAR and Global Navigation Satellite System (GNSS) data when little to no snow is on the ground. Our algorithm automatically post processes the data to generate a labeled traversability map. During this automated process, areas such as grass, sidewalks, stationary obstacles, roads and curb-cuts are identified. By differentiating between these areas using only LIDAR, the vehicle is later able to create a path for travel on only sidewalks or roads and not in other areas. Our localization approach uses an Extended Kalman Filter to fuse the Lightweight and Ground-Optimized LIDAR Odometry and Mapping (LeGO-LOAM) approach with high accuracy GNSS where available, to allow for accurate localization even in areas with poor GNSS, which is often the case in cities and areas covered by tree canopy. This localization approach is used during the data capture stage, prior to the post-processing stage when labeled segmentation is performed, and again during real time autonomous navigation, carried out using the ROS navigation stack. By using LIDAR odometry combined with GNSS, the robot is able to localize under many different weather conditions, including snow and rain, where other algorithms (e.g. AMCL) will likely fail. We were able to successfully have the vehicle autonomously plan and navigate a 1.6km path in an urban snow-covered neighborhood. Our methodology facilitates autonomous navigation functionality under most weather conditions including autonomous wheelchair navigation.
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University of Minnesota M.S.M.E. thesis. April 2020. Major: Mechanical Engineering. Advisor: Max Donath. 1 computer file (PDF); vi, 31 pages.
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Johnson, Reed. (2020). Autonomous Navigation On Urban Sidewalks Under Winter Conditions. Retrieved from the University Digital Conservancy, https://hdl.handle.net/11299/215006.
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