用隐马尔可夫模型实现点云中移动物体的鲁棒分割
Moving Object Segmentation in Point Cloud Data using Hidden Markov Models
- 基于体素的HMM建模，无需学习直接推断动态物体
- 在多个数据集上优于或持平现有方法，泛化能力强
- 适合对实时性与鲁棒性要求高的自动驾驶场景
自主代理需具备识别环境内动态物体的能力以实现安全规划与导航。不完整或错误的动态检测会危及任务完成。由于输入存在多种不确定性且应用场景广泛，动态检测极具挑战性，常导致针对特定场景的解决方案。本文提出一种无需学习的鲁棒方法，用于点云数据中的移动物体分割。该方法以体素为单位建模，采用隐马尔可夫模型（HMM），并通过HMM滤波概率性融合信念生成地图。在多个基准数据集上的测试表明，该方法性能优于或相当于当前最优方法，并在不同传感器特性与环境条件下展现出良好泛化能力。代码已开源：https://github.com/vb44/HMM-MOS。
原文摘要 · Abstract (English)
Autonomous agents require the capability to identify dynamic objects in their environment for safe planning and navigation. Incomplete and erroneous dynamic detections jeopardize the agent's ability to accomplish its task. Dynamic detection is a challenging problem due to the numerous sources of uncertainty inherent in the problem's inputs and the wide variety of applications, which often lead to use-case-tailored solutions. We propose a robust learning-free approach to segment moving objects in point cloud data. The foundation of the approach lies in modelling each voxel using a hidden Markov model (HMM), and probabilistically integrating beliefs into a map using an HMM filter. The proposed approach is tested on benchmark datasets and consistently performs better than or as well as state-of-the-art methods with strong generalized performance across sensor characteristics and environments. The approach is open-sourced at https://github.com/vb44/HMM-MOS.
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