In recent decades, the field of robotic mapping has witnessed widespread research and development in LiDAR (Light Detection And Ranging)-based simultaneous localization and mapping (SLAM) techniques. In this paper, we review the state-of-the-art in LiDAR-based SLAM and explore the remaining challenges that still require attention to satisfy the needs of contemporary applications. A distinctive aspect of this study lies in its literature survey, which specifically investigates the application of various types and configurations of LiDAR, setting it apart from prior reviews. Furthermore, several representative comparisons of LiDAR-based SLAM algorithms are presented, which can serve as a point of reference. Finally, the paper concludes with an insightful discussion on the emergence of new frontiers in the domain of LiDAR-based SLAM.

The efficient use of computing resources on a heterogeneous robotics platform, both in terms of run time performance and power usage, presents an interesting research problem, and is the focus of my research. It is envisaged that this will be achieved by both finding parallel approaches to algorithms commonly used in robotics, and investigating the use of a scheduler to efficiently allocate resources across a heterogeneous hardware platform. In particular, while there has been much research on using specialized hardware for image and video processing algorithms, work on areas specific to robotics, such as position tracking, mapping and sensor fusion, is not as common.