The SLIM system, while designed for scalability and lightweight mapping, may face several limitations in dynamic environments. One significant challenge is the presence of moving objects, such as pedestrians and vehicles, which can introduce noise and inaccuracies in the mapping process. Since SLIM primarily relies on static geometric features (lines and planes), it may struggle to maintain an accurate representation of the environment when dynamic elements are prevalent. This could lead to outdated or incorrect map information, affecting the robot's navigation and localization capabilities.
Additionally, the system's performance may degrade over time due to sensor degradation, which is a common issue in long-term operations. LiDAR sensors can experience changes in calibration, reduced accuracy, or increased noise levels as they age, impacting the quality of the point cloud data. If the SLIM system does not incorporate robust mechanisms for sensor calibration and error correction, the mapping accuracy could suffer, leading to potential failures in navigation tasks.
To mitigate these limitations, the SLIM system could implement adaptive filtering techniques to account for dynamic changes in the environment and sensor performance. This could involve periodically updating the map based on new observations and employing outlier rejection methods to filter out data from moving objects. Furthermore, integrating additional sensors, such as IMUs or cameras, could provide complementary information that enhances the system's resilience to dynamic conditions and sensor degradation.