|Description:||The aim of this project is to review, implement, and compare algorithms for feature selection and outlier rejection in LiDAR-based feature extraction/detection methods. The project will emphasize the comparison of three primary methods for filtering features extracted from LiDAR data. Motivations of the task focus on mitigating ego-motion estimation in LiDAR-based SLAMs under the influence of perceptual aliasing. The student will learn the feature representations and algorithms for evaluating their quality. Verification data will be provided to students by the supervisor. The work will include a review of related literature recommended by the supervisor.