HIF: Height Interval Filtering for Efficient Dynamic Points Removal
This addresses the challenge of real-time dynamic object removal for autonomous navigation systems, representing an incremental improvement in efficiency.
The paper tackles the problem of dynamic object removal in 3D point cloud mapping, which is critical for localization and autonomous navigation, by introducing the Height Interval Filtering (HIF) method, achieving a 7.7 times improvement in time efficiency with comparable accuracy to state-of-the-art methods.
3D point cloud mapping plays a essential role in localization and autonomous navigation. However, dynamic objects often leave residual traces during the map construction process, which undermine the performance of subsequent tasks. Therefore, dynamic object removal has become a critical challenge in point cloud based map construction within dynamic scenarios. Existing approaches, however, often incur significant computational overhead, making it difficult to meet the real-time processing requirements. To address this issue, we introduce the Height Interval Filtering (HIF) method. This approach constructs pillar-based height interval representations to probabilistically model the vertical dimension, with interval probabilities updated through Bayesian inference. It ensures real-time performance while achieving high accuracy and improving robustness in complex environments. Additionally, we propose a low-height preservation strategy that enhances the detection of unknown spaces, reducing misclassification in areas blocked by obstacles (occluded regions). Experiments on public datasets demonstrate that HIF delivers a 7.7 times improvement in time efficiency with comparable accuracy to existing SOTA methods. The code will be publicly available.