Multi-sensor Appearance-Based Place Recognition
Multi-sensor Appearance-Based Place Recognition
复制标题
基于多传感器外观的地点识别
DOI：
10.1109/crv.2013.35
复制
发表时间：
2013
期刊：
影响因子：
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通讯作者：
Vinay Kotamraju
中科院分区：
文献类型：
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作者：
J. Collier;Stephen Se;Vinay Kotamraju
本文描述了一种适用于机器人地图绘制的基于多传感器外观的位置识别系统。与仅从视觉图像中提取特征的系统不同，在这里，我们将众所周知的词袋方法应用于从视觉和距离传感器中提取的特征。通过将这种技术同时应用于两个传感器流，我们可以克服每个单独传感器的缺陷。我们表明，基于激光雷达的地方识别使用的生成模型从可变维局部形状描述符学习，可用于执行地方识别，无论照明条件或大的方向变化，包括遍历循环向后。同样，我们仍然能够利用视觉系统提供的功能丰富的地方识别。使用姿势验证系统，我们能够有效地丢弃假阳性回路检测。我们目前的实验结果，突出了我们的方法的强度，并调查替代技术相结合的结果从各个传感器流。多传感器方法使两个传感器能够在可变照明条件下的大型城市和农村环境中相互补充。
This paper describes a multi-sensor appearance-based place recognition system suitable for robotic mapping. Unlike systems that extract features from visual imagery only, here we apply the well known Bag-of-Words approach to features extracted from both visual and range sensors. By applying this technique to both sensor streams simultaneously we can overcome the deficiencies of each individual sensor. We show that LIDAR-based place recognition using a generative model learnt from Variable Dimensional Local Shape Descriptors can be used to perform place recognition regardless of lighting conditions or large changes in orientation, including traversing loops backward. Likewise, we are still able to exploit the feature rich place recognition that visual systems provide. Using a pose verification system we are able to effectively discard false positive loop detections. We present experimental results that highlight the strength of our approach and investigate alternative techniques for combining the results from the individual sensor streams. The multi-sensor approach enables the two sensors to complement each other well in large urban and rural environments under variable lighting conditions.