Learning data-derived vehicle motion models for use in localisation and mapping

Various solutions to the Simultaneous Localisation and Mapping (SLAM) problem have been proposed over the last 20 years. In particular, extending the fundamental solution of the SLAM problem has attracted a great deal of attention. Most extensions address shortcomings such as data association, computational complexity and improving predictions of a vehicle’s state. However, nearly all SLAM implementations still depend on analytical models to provide estimates for state transitions. Learning data-derived non-analytical models for use during localisation and mapping provides an alternative that could significantly improve estimates and increase the flexibility of models. A methodology to learn motion models without knowledge of the higher-order dynamics is therefore proposed using tapped delay-line neural networks (TDL-NN). Incorporating the learned Nth-order Markov model into a recursive Bayesian estimator requires that the learned model be assumed independent of the transitional model, forming a black box estimator. Both real-world and simulated training data were evaluated, along with changes to the input data’s format, to determine the best vehicle motion predictor. Furthermore, an evaluation methodology is defined to asses how well the models could learn each motion type. A comprehensive analysis of the one-forward prediction using various statistical measures was used to determine the most appropriate metric. The methodology evaluated the predictions at different levels of depth, providing supplementary information on the type of motions that are learnable. Outcomes of the experiments revealed that inherently learning a vehicle’s dynamics cannot be achieved using TDL-NNs. Currently the best that such an approach can learn is the delta between the vehicle’s states. Consequently, modifications are required to the learning algorithms as well as the input data’s format that will force the strategies to learn the higher-order dynamics.Dissertation (MEng)--University of Pretoria, 2018.Electrical, Electronic and Computer EngineeringMEngUnrestricte

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. © 2012 IEEE