Exploiting vehicle motion information in monocular SLAM

It is now well known that increasing the number of features maintained in the mapping process of the monocular SLAM improves the accuracy of the outcome. This, however, increases the state dimension and the associated computational cost. This paper investigates and evaluates the improvement on SLAM results by exploiting camera motion information. For a camera mounted on a vehicle, its motion is subject to the vehicle motion model. The work of this paper shows that by introducing relative pose constraints calculated from image points by considering the underlying vehicle motion model (for example the non-holonomic vehicle motion model), it is possible to incorporate vehicle motion information into the system and achieve even more accurate SLAM results than maintaining all extracted features in the map. It is demonstrated that in this process, the state dimension is not increased, and the sparse structure of the SLAM problem is maintained. So the underlying sparseness in the SLAM problem structure can still be exploited for computational efficiency. Simulation and experiment results are presented to demonstrate the relative merits of incorporating vehicle motion information for motion estimation and mapping.