Foliage discrimination using a rotating ladar

An outdoor environment presents to a robot objects that are drivable, such as tall grass and small bushes, and non-drivable, such as trees and rocks. Due to the difficulty of discriminating between these classes, traditionally a robot searches for paths free of any objects, drivable or not. Although this approach prevents collisions with objects misclassified as drivable, it also eliminates a large number of drivable paths and by doing so, it may eliminate the only path to a desired destination. We present a real time algorithm that detects foliage, using a range from a rotating ladar. Objects not classified as foliage are conservatively labeled as nondrivable obstacles. In contrast to related work that uses range statistics to classify the objects, we exploit the expected localities and continuities of an obstacle, in both space and time. Also, instead of attempting to find a single accurate discriminating factor for every ladar return, we hypothesize the class of some few returns and then spread the confidence (and classification) to other returns using the locality constraints. The Urbie robot is presently using this algorithm to discriminate drivable grass from obstacles during outdoor autonomous navigation tasks.