Absolute localization of ground robots by matching LiDAR and image data in dense forested environments

Abstract

A method for the autonomous geolocation of ground vehicles in forest environments is discussed. 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. Extraction of stems from 3D data is achieved by using Support Vector Machine (SVM) classifiers and height above ground filters that separate ground points from vertical stem features. Identification of stems from overhead imagery is achieved by finding the centroids of tree crowns extracted using a watershed segmentation algorithm. Matching of the two maps is achieved by using a robust Iterative Closest Point (ICP) algorithm that determines the rotation and translation vectors to align the datasets. The alignment is used to calculate the absolute horizontal location of the vehicle. 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. It is noted that the algorithm’s accuracy performance is currently limited by the accuracy and resolution of aerial orthoimagery used. The method can be used in real-time as a complement to the Global Positioning System (GPS) in areas where signal coverage is inadequate due to attenuation by the forest canopy, or due to intentional denied access. The method has two key properties that are significant: i) It does not require a priori knowledge of the area surrounding the robot. ii) Uses the geometry of detected tree stems as the only input to determine horizontal geoposition.