Research Article
Fusion Algorithms and Anti-Interference Stability Optimization for SLAM in Inspection UAVs under Complex Occlusion Conditions
@INPROCEEDINGS{10.4108/eai.24-4-2026.2364942, author={Chengzhan Li and Rui Peng}, title={Fusion Algorithms and Anti-Interference Stability Optimization for SLAM in Inspection UAVs under Complex Occlusion Conditions}, proceedings={Proceedings of the 3rd International Conference on Mechanics, Electronics Engineering and Automation, ICMEEA 2026, April 24-26, 2026, Singapore, Singapore}, publisher={EAI}, proceedings_a={ICMEEA}, year={2026}, month={9}, keywords={Drones Multi-sensor fusion Anti-interference SLAM technology}, doi={10.4108/eai.24-4-2026.2364942} }- Chengzhan Li
Rui Peng
Year: 2026
Fusion Algorithms and Anti-Interference Stability Optimization for SLAM in Inspection UAVs under Complex Occlusion Conditions
ICMEEA
EAI
DOI: 10.4108/eai.24-4-2026.2364942
Abstract
To solve the positioning drift and control response inaccuracy problems that are prevalent in Simultaneous Localization and Mapping (SLAM) of inspection UAVs in complex occlusion and multi-interference coupled situations, this paper systematically reviews multi-sensor fusion SLAM algorithms and their anti-interference stability optimization. It first examines the technical aspects and shortcomings of visual SLAM and LiDAR SLAM and covers multi-sensor fusion schemes like vision-LiDAR and LiDAR-IMU. Secondly, this paper summarizes a collaborative framework on the basis of filtering-based state sharing, model predictive control (MPC) integration, and event-triggered mechanisms to resolve the problem of inadequate coordination between SLAM and the control system. Thirdly, it concludes an anti-interference plan which incorporates both conventional robust control and learning-reinforced control. The results of this review offer technical assistance to enhance the working reliability of inspection drones in complex environments.
