On Landmark Densities in Minimum-Uncertainty Motion Planning

Abstract: Accurate self-positioning of autonomous mobile platforms is important when performing tasks such as target tracking, reconnaissance and resupply missions. Without access to an existing positioning infrastructure, such as Global Navigation Satellite Systems (GNSS), the platform instead needs to rely on its own sensors to obtain an accurate position estimate. This can be achieved by detecting and tracking landmarks in the environment using techniques such as simultaneous localization and mapping (SLAM). However, landmark-based SLAM approaches do not perform well in areas without landmarks or when the landmarks do not provide enough information about the environment. It is therefore desirable to estimate and minimize the position uncertainty while planning how to perform the task. A complicating factor is that the landmarks used in SLAM are not known at the time of planning.In this thesis, it is shown that by integrating SLAM and path planning, paths can be computed that are favorable, from a localization point of view, during motion execution. In particular, it is investigated how prior knowledge of landmark distributions, or densities, can be used to predict the information gained from a region. This is done without explicit knowledge of landmark positions. This prediction is then integrated into the path-planning problem.The first contribution is the introduction of virtual landmarks which represent the expected information in unexplored regions during planning. Two approaches to construct the virtual landmarks that capture the expected information available, based on the beforehand known landmark density, are given. The first approach can be used with any sensor configuration while the second one uses properties of range-bearing sensors, such as LiDAR sensors, to improve the quality of the approximation.The second contribution is a methodology for generating landmark densities from prior data for a forest scenario. These densities were generated from publicly available aerial data used in the Swedish forest industry.The third contribution is an approach to compute the probability of detecting pole-based landmarks in LiDAR point clouds. The approach uses properties of the sensor, the landmark detector, and the probability of occlusion from other landmarks in order to model the detection probability. The model accuracy has been validated in simulations where a real landmark detector and simulated Li-DAR point clouds have been used in a forest scenario.The final contribution is a position-uncertainty aware path-planning approach. This approach utilizes virtual landmarks, the landmark densities, and the land-mark detection probabilities, to produce paths which are advantageous from a positioning point of view. The approach is shown to reduce the platform position uncertainty in several different simulated scenarios without prior knowledge of explicit landmark positions. The computed position uncertainty is shown to be relatively comparable to the uncertainty obtained when executing the path. Furthermore, the generated paths show characteristics that make sense from an application point of view.