School of Mechanical Engineering and Automation, Beihang University, Beijing 100191, China.
State Key Laboratory of Virtual Reality Technology and Systems, Beihang University, Beijing 100191, China.
Sensors (Basel). 2019 Jan 10;19(2):249. doi: 10.3390/s19020249.
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.
本文提出了一种新颖的基于多传感器的室内全局定位系统，该系统将基于卷积神经网络（CNN）的图像检索辅助视觉定位与概率定位方法相结合。全局定位系统由三部分组成：粗定位识别、精确定位和从绑架中重新定位。粗定位识别利用单目相机基于图像检索实现初始定位，其中采用预训练的卷积神经网络（CNN）提取的现成特征来确定机器人的候选位置。在精确定位中，配备了激光测距仪，通过自适应蒙特卡罗定位来估计移动机器人的精确姿态，其中图像检索获得的候选位置被视为初始随机采样的种子。此外，为了解决机器人绑架问题，我们提出了一种闭环定位机制，以便实时监控机器人的状态，并在机器人被绑架时进行自适应调整。闭环机制有效地利用图像序列的相关性，基于长短期记忆（LSTM）网络实现重新定位。进行了广泛的实验，结果表明，与两种传统的定位方法相比，所提出的方法不仅在准确性和速度方面有了很大的提高，而且还可以从定位失败中恢复。
