Browsing by Author "Gericke, Johannes Petrus"
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- ItemSLAM landmark identification using lidar measurements(Stellenbosch : Stellenbosch University, 2019-04) Gericke, Johannes Petrus; Van Daalen, C. E.; Stellenbosch University. Faculty of Engineering. Dept. of Electrical and Electronic Engineering.ENGLISH ABSTRACT: This thesis investigates the use of Lidar sensors for landmark identification in simultaneous localisation and mapping (SLAM). Lidar sensors can very accurately measure the distance toward objects in the environment, but provide no other information about the environment. Modelling landmarks from this spatial representation and associating new measurements with landmarks are important problems to address in order to perform SLAM with these measurements. A literature review shows that multiple different approaches to extracting features from Lidar measurements and modelling landmarks exist. From this study we conclude that existing methods do not reliably associate measurements to landmarks and do not have the ability to update landmark models, which could be helpful to improve the representation of the environment. We consequently develop a probabilistic method to model landmarks from Lidar measurements. In our approach, we approximate object boundaries with continuous, piecewise linear functions. The parameters of these functions are modelled as Gaussian random variables. With this probabilistic model, a method is created to determine if measurements originate from a certain landmark. This method first aligns the measurements to the model using the iterated closest point (ICP) algorithm and then it finds the Mahalanobis distance between measurements and lines to determine if the measurements fit the model. A method is also developed to probabilistically update the model and extend the model when new segments of the landmark are observed. In addition, a SLAM algorithm is designed to use this landmark modelling method. The extended Kalman filter (EKF) SLAM motion update is used directly with an odometry motion model. The measurement update, however, is adapted in order to update the model parameters and robot pose simultaneously. This is in contrast to most other approaches that only use the landmark model to extract a point or reference measurement. Finally, our methods are tested in both simulated environments and with realworld datasets. The results show that the landmark models are good representations of the environment and that measurements of unique objects can be associated with their models. However, the robot tends to be overconfident about its pose and fails to close bigger loops due to faulty associations. We conclude that this approach successfully models the environment with SLAM, but further development needs to take place to make it robust and suitable for practical applications.