Precise self-localization of a walking robot on rough terrain using parallel tracking and mapping

Purpose – The purpose of this paper is to describe a novel application of the recently introduced concept from computer vision to self‐localization of a walking robot in unstructured environments. The technique described in this paper enables a walking robot with a monocular vision system (single camera) to obtain precise estimates of its pose with regard to the six degrees of freedom. This capability is essential in search and rescue missions in collapsed buildings, polluted industrial plants, etc.Design/methodology/approach – The Parallel Tracking and Mapping (PTAM) algorithm and the Inertial Measurement Unit (IMU) are used to determine the 6‐d.o.f. pose of a walking robot. Bundle‐adjustment‐based tracking and structure reconstruction are applied to obtain precise camera poses from the monocular vision data. The inclination of the robot's platform is determined by using IMU. The self‐localization system is used together with the RRT‐based motion planner, which allows to walk autonomously on rough, previ...