用粒子滤波实现动态环境中的实例级语义占位映射
Particle-based Instance-aware Semantic Occupancy Mapping in Dynamic Environments
- 用带实例状态的粒子估计物体概率密度，隐式建模环境
- 在虚拟KITTI2上优于现有方法，噪声下仍保持稳定性能
- 适合需要精准感知动态物体的机器人交互任务
在动态环境中，以实例级语义与几何信息表征3D环境对交互式机器人至关重要。然而，传感器噪声、实例分割与跟踪误差以及物体动态运动给构建此类表示带来挑战。本文提出一种基于粒子的实例级语义占位映射方法。通过引入增强实例状态的粒子，估计物体的概率假设密度（PHD），并隐式建模环境。利用状态增强的顺序蒙特卡洛PHD（S²MC-PHD）滤波器，粒子被更新以联合估计占位状态、语义类别和实例ID，有效缓解噪声影响。此外，采用记忆模块提升地图对已观测物体的响应能力。在Virtual KITTI 2数据集上的实验表明，该方法在不同噪声条件下均超越现有最先进方法，多个指标表现更优。后续真实数据测试进一步验证了其有效性。
原文摘要 · Abstract (English)
Representing the 3D environment with instance-aware semantic and geometric information is crucial for interaction-aware robots in dynamic environments. Nevertheless, creating such a representation poses challenges due to sensor noise, instance segmentation and tracking errors, and the objects' dynamic motion. This paper introduces a novel particle-based instance-aware semantic occupancy map to tackle these challenges. Particles with an augmented instance state are used to estimate the Probability Hypothesis Density (PHD) of the objects and implicitly model the environment. Utilizing a State-augmented Sequential Monte Carlo PHD (S$^2$MC-PHD) filter, these particles are updated to jointly estimate occupancy status, semantic, and instance IDs, mitigating noise. Additionally, a memory module is adopted to enhance the map's responsiveness to previously observed objects. Experimental results on the Virtual KITTI 2 dataset demonstrate that the proposed approach surpasses state-of-the-art methods across multiple metrics under different noise conditions. Subsequent tests using real-world data further validate the effectiveness of the proposed approach.
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