Reliability Estimation of Vehicle Localization Result
Reliability Estimation of Vehicle Localization Result
复制标题
DOI：
10.1109/ivs.2018.8500625
复制
发表时间：
2018-06
期刊：
影响因子：
--
通讯作者：
Naoki Akai;Luis Yoichi Morales Saiki;H. Murase
中科院分区：
文献类型：
--
作者：
Naoki Akai;Luis Yoichi Morales Saiki;H. Murase
本文提出了一种车辆定位结果可靠性估计方法。我们之前提出了一种使用卷积神经网络（CNN）的室内移动机器人故障检测方法。由于图像数据通常被馈送到 CNN，因此我们将从机器人位姿、占用网格图和激光扫描数据获得的图像数据馈送到 CNN，由 CNN 来决定定位是否失败。先前的方法还采用 Rao-Blackwellized 粒子滤波器来同时估计机器人位姿和该估计的可靠性。然而，车辆机器人很难使用以前的方法，因为创建和处理图像数据不是一个简单的计算过程。在本研究中，我们通过改进输入 CNN 的数据来扩展之前的方法，从而使车辆机器人能够同时执行定位和估计。本文详细描述了同时估计，并表明可靠性可以用作检测定位失败的精确标准。关键词-车辆定位、可靠性
This paper proposes a method for estimation of the reliability of vehicle localization results. We previously proposed a fault detection method for indoor mobile robots using a convolutional neural network (CNN). Because image data is generally fed to a CNN, we feed image data obtained from the robot pose, occupancy grid map, and laser scan data to the CNN, which decides of whether localization has failed. The previous method also employed a Rao-Blackwellized particle filter to estimate the robot pose and reliability of this estimation simultaneously. However, it was difficult for vehicle robots to use the previous method as creating and processing image data is not a light computation process. In this study, we extend the previous method by improving the data fed to the CNN, thus making it possible for vehicle robots to perform simultaneous localization and estimation. This paper describes in detail the simultaneous estimation and shows that the reliability can be used as an exact criterion for detecting localization failures. Keywords-Vehicle Localization, Reliability