基于不确定性建模的3D场景感知方法

A 3D Scene Perception Method Based on Uncertainty Modeling

  • 摘要: 本文旨在解决多任务场景感知中存在的局限性,包括缺乏对检测、跟踪、建图、定位等任务的状态估计不确定性的系统建模分析。将场景感知任务划分为前景感知和背景感知,前者主要涉及前景目标的检测与跟踪,而后者专注于机器人的定位和建图任务。为了实现对复杂场景的多任务感知,将二者融合于动态贝叶斯网络框架下,将多任务场景感知问题建模为系统状态参数的联合优化估计问题,采用贝叶斯后验概率估计对各系统参数状态进行建模。从LiDAR(激光雷达)传感器的点云测量噪声入手,分析了目标检测与跟踪网络中真值标注和自身姿态估计的不确定性并构建了点云测量及标签的不确定性模型和基于预测置信度的跟踪模型,同时分析了定位误差对建图不确定性及目标跟踪任务的影响,使用迭代扩展卡尔曼滤波对姿态最大后验概率估计进行优化。本文方法可实现复杂大规模动态环境的场景感知,在KITTI和UrbanNav数据集上的实验结果表明,本文方法有效解决了复杂场景下动态目标对环境建图的影响,具有高精度和鲁棒性。

     

    Abstract: This paper aims to address limitations in multi-task scene perception, including the lack of system modeling and analysis on the uncertainty in state estimation for tasks such as detection, tracking, mapping, and localization. The scene perception task is divided into foreground and background perception tasks, where the former involves the detection and tracking of foreground objects, and the latter focuses on robot localization and mapping tasks. To achieve multi-task perception in complex scenes, both tasks are integrated within a dynamic Bayesian network framework. The multi-task scene perception problem is modeled as a joint optimization and estimation problem of system state parameters. Bayesian posterior probability estimation is employed to model the state of each system parameter. Starting with the point cloud measurement noises from LiDAR sensors, the uncertainties in ground truth annotation and self-pose estimation in the object detection and tracking network are analyzed, and uncertainty models for point cloud measurements and labels are constructed, along with a tracking model based on prediction confidence. Additionally, the impact of localization errors on mapping uncertainty and target tracking tasks is analyzed. An iterative extended Kalman filter is used to optimize the estimation of the pose's maximum posterior probability. The proposed method achieves scene perception in complex and large-scale dynamic environments. Experimental results on the KITTI and UrbanNav datasets demonstrate its effectiveness in addressing the impact of dynamic targets on environmental mapping in complex scenes, with high accuracy and robustness.

     

/

返回文章
返回