Published in , 2024
- Abstract—In general, vision-based SLAM struggles to detect dynamic objects, which complicates tracking for Unmanned Ground Vehicles (UGVs). This issue arises because vision-based SLAM is susceptible to environmental factors such as shadows or significant changes in illumination, which can affect object detection. Particularly, raw data that excludes dynamic objects but includes shadows does not accurately represent the real environment. Our objective is to implement a shadow removal algorithm that addresses both static and dynamic objects at the front end of the SLAM pipeline to see whether it improves our SLAM accuracy results.
- Index Terms: Ground Vehicle, Vision-based SLAM, Shadow Removal
Recommended citation: Wan, H., Kusumadjaja, K., Lee, S.H., & Do, T. (2024). Enhancing Vision-based SLAM through Shadow Removal Preprocessing. Unpublished manuscript, University of Michigan, Ann Arbor.
