Bayesian Place Recognition Based on Bag of Objects for Intelligent Vehicle Localization
Bayesian Place Recognition Based on Bag of Objects for Intelligent Vehicle Localization
复制标题
基于物体袋的贝叶斯地点识别用于智能车辆定位
DOI：
10.1049/iet-its.2018.5431
复制
发表时间：
2019
影响因子：
2.7
通讯作者：
穆孟超
中科院分区：
文献类型：
--
作者：
王相龙;胡钊政;陶倩文;黄 刚;穆孟超
地点识别是从预先生成的图像序列中匹配出相应的图像，在基于视觉地图的车辆定位中起着重要的作用。提出了一种基于一袋物体的贝叶斯位置识别算法。提出的BoO是上级的经典词袋（BoW），具有更快的速度和更大的鲁棒性。场景表示。像BoW一样，BoO使用从预训练的AlexNet计算的对象概率来构建一个类似直方图的.vector，称为BoO特征。从地图数据中计算出一小部分对象，称为主导对象，以减少存储需求。BoO特征用于描述场景图像的相似性。由于AlexNet出色的识别性能，BoO功能对动态照明环境具有鲁棒性。采用BoO算法，employed.to物体的外观特征和运动线索融合在一起进行位置识别.此外，多尺度定位策略的发展。提高位置识别效率。所提出的方法进行了测试与收集的数据和KITTI数据集。该方法实现了0.37帧错误，平均花费4.7毫秒的位置识别。实验结果表明，该方法优于经典BoW和二进制BoW。
Place recognition matches a corresponding image from a pre-built image sequence, and it plays an important role in.visual map-based vehicle localisation. This study proposes a Bayesian place recognition (BPR) algorithm based on a bag of.objects (BoO). The proposed BoO is superior to the classic bag of words (BoW), with a faster speed and greater robustness for.scene representation. Like BoW, BoO uses object probabilities computed from the pre-trained AlexNet to construct a histogramlike.vector called the BoO feature. A small portion of objects, named dominant objects, are computed from map data to reduce.the storage requirement. The BoO feature is used to describe the scene image similarity. Owing to the excellent recognition.performance of AlexNet, the BoO feature is robust to dynamic illumination environments. With BoO, the BPR model is employed.to fuse appearance feature and motion clue for place recognition. Moreover, a multi-scale localisation strategy is developed to.improve the place recognition efficiency. The proposed method was tested with the collected data and the KITTI datasets. The.method achieved 0.37 frame errors, taking an average of 4.7 ms for place recognition. Experimental results demonstrated that.the proposed method outperforms the classic BoW and binary BoW.