Gostar Amirali Khodadadian, Fu Chunyun, Chuah Weiqin, Hossain Mohammed Imran, Tennakoon Ruwan, Bab-Hadiashar Alireza, Hoseinnezhad Reza
School of Engineering, RMIT University, Melbourne VIC 3001, Australia.
State Key Laboratory of Mechanical Transmissions, School of Automotive Engineering, Chongqing University, Chongqing 400044, China.
Sensors (Basel). 2019 Apr 3;19(7):1614. doi: 10.3390/s19071614.
There is a large body of literature on solving the SLAM problem for various autonomous vehicle applications. A substantial part of the solutions is formulated based on using statistical (mainly Bayesian) filters such as Kalman filter and its extended version. In such solutions, the measurements are commonly some point features or detections collected by the sensor(s) on board the autonomous vehicle. With the increasing utilization of scanners with common autonomous cars, and availability of 3D point clouds in real-time and at fast rates, it is now possible to use more sophisticated features extracted from the point clouds for filtering. This paper presents the idea of using planar features with multi-object Bayesian filters for SLAM. With Bayesian filters, the first step is prediction, where the object states are propagated to the next time based on a stochastic transition model. We first present how such a transition model can be developed, and then propose a solution for state prediction. In the simulation studies, using a dataset of measurements acquired from real vehicle sensors, we apply the proposed model to predict the next planar features and vehicle states. The results show reasonable accuracy and efficiency for statistical filtering-based SLAM applications.
关于为各种自动驾驶车辆应用解决同步定位与地图构建（SLAM）问题，有大量的文献。很大一部分解决方案是基于使用统计（主要是贝叶斯）滤波器制定的，如卡尔曼滤波器及其扩展版本。在这类解决方案中，测量通常是自动驾驶车辆上的传感器收集的一些点特征或检测结果。随着普通自动驾驶汽车对扫描仪的使用日益增加，以及实时且快速地获取三维点云，现在可以使用从点云中提取的更复杂的特征进行滤波。本文提出了将平面特征与多目标贝叶斯滤波器用于SLAM的想法。对于贝叶斯滤波器，第一步是预测，即基于随机转移模型将目标状态传播到下一个时刻。我们首先展示如何开发这样的转移模型，然后提出一种状态预测的解决方案。在仿真研究中，使用从实际车辆传感器获取的测量数据集，我们应用所提出的模型来预测下一个平面特征和车辆状态。结果表明，对于基于统计滤波的SLAM应用，具有合理的准确性和效率。
