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

Abstract

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.