Machine Learning for LiDAR-SLAM : In Forest Terrains

Abstract: Point set registration is a well-researched yet still not a very exploited area in computer vision. As the field of machine learning grows, the possibilities of application expand. This thesis investigates the possibility to expand an already implemented probabilistic machine learning approach to point set registration to more complex, larger datasets gathered in a forest environment. The system used as a starting point was created by Järemo Lawin et. al. [10]. The aim of the thesis was to investigate the possibility to register the forest data with the existing system, without ground-truth poses, with different optimizers, and to implement a SLAM pipeline. Also, older methods were used as a benchmark for evaluation, more specifically iterative closest point(ICP) and fast global registration(FGR).To enable the gathered data to be processed by the registration algorithms, preprocessing was required. Transforming the data points from the coordinate system of the sensor to world relative coordinates via LiDAR base coordinates. Subsequently, the registration was performed with different approaches. Both the KITTI odometry dataset, which RLLReg originally was evaluated with[10], and the gathered forest data were used. Data augmentation was utilized to enable ground-truth-independent training and to increase diversity in the data. In addition, the registration results were used to create a SLAM-pipeline, enabling mapping and localization in the scanned areas. The results showed great potential for using RLLReg to register forest scenes compared to other, older, approaches. Especially, the lack of ground-truth was manageable using data augmentation to create training data. Moreover, there was no evidence that AdaBound improves the system when replacing the Adam-optimizer. Finally, forest models with sensor paths plotted were generated with decent results. However, a potential for post-processing with further refinement is possible. Nevertheless, the possibility of point set registration and LiDAR-SLAM using machine learning has been confirmed.