Satellite Image Informed Path Planning for Improved Visual Localization of Unmanned Ground Vehicles in GNSS-Denied Environments

Jordan Westphal, Arturo Saucedo, Maribel DeLaTorre, Ahmet Soylemezoglu

Peer Reviewed

Abstract:	GNSS-denied and long-range unmanned ground vehicle (UGV) operations often occur in environments such as roads, grasslands, and deserts where a lack of visual features in the environment is an issue for visual localization. Common SLAM-based UGV navigation methods in GNSS-denied environments employ LiDAR, radar, or camera odometry, which drift significantly in visually uninformative environments. We present a path-planning approach utilizing a priori satellite imagery to maximize navigation in areas with high visual uniqueness. Offline, iterative template matching is performed on the satellite image to generate a map of visual uniqueness in the environment, utilized as a costmap to the global planner. The uniqueness map was tested on an autonomous UGV in a visually informative environment and a visually uninformative environment based on visual odometry dead-reckoning. Neutral performance was observed in the visually informative environment, while a 55% reduction in visual odometry drift from the ground truth was observed in the visually uninformative environment utilizing this approach.
Published in:	2025 IEEE/ION Position, Location and Navigation Symposium (PLANS) April 28 - 1, 2025 Salt Lake Marriott Downtown at City Creek Salt Lake City, UT
Pages:	831 - 836
Cite this article:	Westphal, Jordan, Saucedo, Arturo, DeLaTorre, Maribel, Soylemezoglu, Ahmet, "Satellite Image Informed Path Planning for Improved Visual Localization of Unmanned Ground Vehicles in GNSS-Denied Environments," 2025 IEEE/ION Position, Location and Navigation Symposium (PLANS), Salt Lake City, UT, April 2025, pp. 831-836.
Full Paper:	ION Members/Non-Members: 1 Download Credit Sign In