Visual Odometry for Tracked Vehicles
Conference paper (Refereed)
In Proc. of the IEEE Int. Workshop on Safety, Security and Rescue Robotics (SSRR)
Artificial Intelligence & Integrated Computer Systems
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 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.