Global Localization of Autonomous Robots in Forest Environments

Abstract

Abstract A method for the autonomous geolocation of ground vehicles in forest environments is presented. The method provides an estimate of the global horizontal position of a vehicle strictly based on finding a geometric match between a map of observed tree stems, scanned in 3D by Light Detection and Ranging (lidar) sensors onboard the vehicle, to another stem map generated from the structure of tree crowns analyzed from high resolution aerial orthoimagery of the forest canopy. The method has been tested with real-world data and has been able to estimate vehicle geoposition with an average error of less than 2 m. The method has two key properties that are significant: (a) The algorithm does not require a priori knowledge of the area surrounding the robot, and (b) Based on estimated vehicle state, it uses the geometry of detected tree stems as the only input to determine horizontal geoposition.