HIF: Height Interval Filtering for Efficient Dynamic Points Removal

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.

HyperGraph ROS: An Open-Source Robot Operating System for Hybrid Parallel Computing based on Computational HyperGraph

This paper presents HyperGraph ROS, an open-source robot operating system that unifies intra-process, inter-process, and cross-device computation into a computational hypergraph for efficient message passing and parallel execution. In order to optimize communication, HyperGraph ROS dynamically selects the optimal communication mechanism while maintaining a consistent API. For intra-process messages, Intel-TBB Flow Graph is used with C++ pointer passing, which ensures zero memory copying and instant delivery. Meanwhile, inter-process and cross-device communication seamlessly switch to ZeroMQ. When a node receives a message from any source, it is immediately activated and scheduled for parallel execution by Intel-TBB. The computational hypergraph consists of nodes represented by TBB flow graph nodes and edges formed by TBB pointer-based connections for intra-process communication, as well as ZeroMQ links for inter-process and cross-device communication. This structure enables seamless distributed parallelism. Additionally, HyperGraph ROS provides ROS-like utilities such as a parameter server, a coordinate transformation tree, and visualization tools. Evaluation in diverse robotic scenarios demonstrates significantly higher transmission and throughput efficiency compared to ROS 2. Our work is available at https://github.com/wujiazheng2020a/hyper_graph_ros.