Visual Odometry for Tracked Vehicles

Localization and mapping on autonomous robots typically requires a good pose estimate, which is hard to acquire if the vehicle is tracked. In this paper we describe a solution to the pose estimation problem by utilizing a consumer-quality camera and an Inertial Measurement Unit (IMU). The basic idea is to continuously track salient features with the KLT feature tracker [12] over multiple images taken by the camera and to extract from the tracked features image vectors resulting from the robot’s motion. Each image vector is taken for a voting that best explains the robot’s motion. Image vectors vote according to a previously trained tile coding classificator that assigns to each possible image vector a translation probability. Our results show that the proposed single camera solution leads to sufficiently accurate pose estimates of the tracked vehicle.