【读论文】变化环境的Visual place recognition_visual place recognition的代表论文

【读论文】Use of Roadway Scene Semantic Information and Geometry-Preserving Landmark Pairs to Improve Visual Place Recognition in Changing Environments
2017年IEEE收录
摘要 —— Visual place recognition (VPR) in changing environments is an urgent challenge for long-term autonomous navigation. One recent ConvNet landmark-based approach exploits region landmarks coupled with ConvNet features to match images, and the approach has shown promising results under significant environmental and viewpoint changes. In this paper, we propose a robust ConvNet landmark-based system for VPR in changing outdoor roadway environments by extension of this approach from the following two aspects. First, our method utilizes more discriminative landmarks obtained by a novel refinement method called SemLandmarks, which leverages roadway scene semantic information to screen landmarks directly detected by an existing object proposal method.Second, our method improves the accuracy of image matching by introducing consistent spatial constraints based on the use of geometry-preserving landmark pairs. Experimental results demonstrate that our method significantly improves the state of the art in VPR in terms of recognition accuracy on three challenging benchmark data sets with various environmental and viewpoint changes.

        在不断变化的环境中，视觉位置识别（VPR）是一项长期自主导航的紧迫挑战。最近ConvNet基于地标的一种方法利用与ConvNet特征相结合的区域标志来匹配图像，并且该方法在显着的环境和视点变化下显示出有希望的结果。在本文中，我们提出了一个强大的ConvNet基于地标的VPR系统。室外道路环境通过以下两个方面的延伸来实现。首先，我们的方法利用了一种名为SemLandmarks的新型改进方法获得的更具辨别力的地标，利用道路场景语义信息来筛选现有对象提议方法直接检测到的地标。其次，我们的方法通过引入一致的基于使用几何保留的地标对的约束空间来提高图像匹配的准确性。实验结果表明，我们的方法在具有各种环境和视点变化的三个具有挑战性的基准数据集的识别准确性方面显着改善了VPR的现有技术水平。

介绍

         主要介绍视觉位置识别的难点在于 condition invariance（环境变化） 和 viewpoint invariance（视角变化）。相比于传统手工提取特征，卷积神经网络提取特征主要提取图像中地标，增强在环境视角变化的鲁棒性。作者算法的改进主要来自于15年的一篇论文：N. Süenderhauf et al., ``Place recognition with ConvNet landmarks:Viewpoint-robust, condition-robust, training-free,’’ in Proc. Robot., Sci.Syst. (RSS),