A Robust Indoor Localization System Integrating Visual Localization Aided by CNN-Based Image Retrieval with Monte Carlo Localization
A Robust Indoor Localization System Integrating Visual Localization Aided by CNN-Based Image Retrieval with Monte Carlo Localization
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DOI：
10.3390/s19020249
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发表时间：
2019-01
期刊：
影响因子：
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通讯作者：
S. Xu;Wusheng Chou;Hongyi Dong
中科院分区：
文献类型：
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作者：
S. Xu;Wusheng Chou;Hongyi Dong
本文提出了一种新的基于多传感器的室内全球定位系统集成视觉定位辅助基于CNN的图像检索与概率定位方法。全局定位系统由三部分组成：粗定位、精定位和绑架重定位。粗位置识别利用单目摄像机实现基于图像检索的初始定位，其中采用从预训练的卷积神经网络（CNN）中提取的现成特征来确定机器人的候选位置。在精定位中，利用激光测距仪对移动的机器人进行精确位姿估计，并将图像检索得到的候选位置作为初始随机采样的种子，进行自适应蒙特卡罗定位。此外，为了解决机器人绑架的问题，我们提出了一个闭环定位机制，以监测机器人的状态在真实的时间，并作出自适应调整时，机器人被绑架。闭环机制有效地利用图像序列的相关性，实现基于长短期记忆（LSTM）网络的重定位。实验结果表明，与传统的两种定位方法相比，该方法不仅在定位精度和定位速度上有较大的提高，而且能够从定位失败中恢复。
This paper proposes a novel multi-sensor-based indoor global localization system integrating visual localization aided by CNN-based image retrieval with a probabilistic localization approach. The global localization system consists of three parts: coarse place recognition, fine localization and re-localization from kidnapping. Coarse place recognition exploits a monocular camera to realize the initial localization based on image retrieval, in which off-the-shelf features extracted from a pre-trained Convolutional Neural Network (CNN) are adopted to determine the candidate locations of the robot. In the fine localization, a laser range finder is equipped to estimate the accurate pose of a mobile robot by means of an adaptive Monte Carlo localization, in which the candidate locations obtained by image retrieval are considered as seeds for initial random sampling. Additionally, to address the problem of robot kidnapping, we present a closed-loop localization mechanism to monitor the state of the robot in real time and make adaptive adjustments when the robot is kidnapped. The closed-loop mechanism effectively exploits the correlation of image sequences to realize the re-localization based on Long-Short Term Memory (LSTM) network. Extensive experiments were conducted and the results indicate that the proposed method not only exhibits great improvement on accuracy and speed, but also can recover from localization failures compared to two conventional localization methods.