This paper addresses the problem of simultaneous localization and mapping (SLAM) for the case of very small, resource-limited robots which have poor odometry and can typically only carry a single monocular camera. We propose a modification to the standard SLAM algorithm in which the assumption that the robots can obtain metric distance/bearing information to landmarks is relaxed. Instead, the robot registers a distinctive sensor "signature", based on its current location, which is used to match robot positions. In our formulation of this non-linear estimation problem, we infer implicit position measurements from an image recognition algorithm. The iterated form of the extended Kalman filter (IEKF) is employed to process all measurements.
['Paul E. Rybski', 'Stergios I. Roumeliotis', 'Maria L. Gini', 'Nikolaos Papanikolopoulos']
Appearance-based minimalistic metric SLAM