Map-aided 6-DOF relative pose estimation for monocular SLAM using sparse information filters

This paper addresses the problem of mapping three dimensional environments from a sequence of images taken by a calibrated camera, and simultaneously generating the camera motion trajectory. This is the Monocular SLAM problem in robotics, and is akin to the Structure from Motion (SFM) problem in computer vision. We present a novel map-aided 6-DOF relative pose estimation method based on a new formulation of the Monocular SLAM that is able to provide better initial estimates of new camera poses than the simple triangulation traditionally used in this context. The ´6-DOF´ means relative to the map which itself is up to an unobservable scale. The proposed pose estimator also allows more effective outlier rejection in matching features present in the map and features extracted from two consecutive images. Our Monocular SLAM algorithm is able to deal with arbitrary camera motion, making the smooth motion assumption, which is required by the typically used constant velocity model, unnecessary. In the new Monocular SLAM formulation, the measurements of extracted features from images are partitioned into those used for the estimation of the environment and those used for estimating the camera motion. The new formulation enables the current map estimate to aid achieving the full 6-DOF relative pose estimation up to the mapping scale while maximally exploiting the geometry information in images. Experiment results are provided to verify the proposed algorithm.