Integrating Photogrammetry and Inertial Sensors for Robotics Navigation and Mapping

Abstract

Abstract: Integrating visual and inertial sensors has become a common practice in navigation due to the increase in computer power, in algorithms advancement and in sensor improvements. One of the problems yet to be solved is the Simultaneous Localisation And Mapping (SLAM). SLAM is a term used in the robotics community to describe the problem of mapping the environment and at the same time using this map to determine (or to help in determining) the location of the mapping device. Classically, terrestrial robotics SLAM is approached using LASER scanners to locate the robot relative to the structured environment and at the same time to map this environment; however, outdoors robotics SLAM is not feasible with LASER. Recently, the use of visual methods, integrated with inertial sensors, has gained an interest. The current solutions use a single Kalman Filter with a state vector containing the map and the robot coordinates, which introduces non-linearity and complications to the filter, which then needs to run at high rates (20 Hz) with simplified navigation models. In this study, SLAM is developed using the Geomatics Engineering approach. Two filters are used in parallel: the Least-Squares Adjustment (LSA) for mapping and the Kalman Filter (KF) for navigation. Conceptually, the outputs of the LSA photogrammetric resection (position and orientation) are used as the KF