Visual Place Recognition via Robust ℓ2-Norm Distance Based Holism and Landmark Integration
Visual Place Recognition via Robust ℓ2-Norm Distance Based Holism and Landmark Integration
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DOI：
10.1609/aaai.v33i01.33018034
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发表时间：
2019-07
期刊：
影响因子：
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通讯作者：
Kai Liu;Hua Wang;Fei Han;Hao Zhang
中科院分区：
文献类型：
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作者：
Kai Liu;Hua Wang;Fei Han;Hao Zhang
视觉位置识别是大规模同步定位与制图（SLAM）的基础。机器人在日、月和季节的不同时间进行长期操作，会带来重大环境变化带来的新挑战。在本文中，我们提出了一种新的学习位置表示的方法，该方法可以将一个地方的语义地标与它的整体表示相结合。为了提高我们的新模型对由于长期视觉变化引起的剧烈外观变化的鲁棒性，我们制定了使用非平方的2-范数距离的目标，这导致了一个困难的优化问题，即最小化矩阵的1,2 -范数的比率。为了解决这一问题，我们导出了一种新的高效迭代算法，该算法的收敛性得到了严格的理论保证。此外，由于我们的解决方案是严格正交的，学习到的位置表示可以有更好的位置识别能力。我们使用两个大规模基准数据集（CMU-VL和Nordland数据集）来评估所提出的方法。实验结果验证了该方法在长期视觉位置识别中的有效性。
Visual place recognition is essential for large-scale simultaneous localization and mapping (SLAM). Long-term robot operations across different time of the days, months, and seasons introduce new challenges from significant environment appearance variations. In this paper, we propose a novel method to learn a location representation that can integrate the semantic landmarks of a place with its holistic representation. To promote the robustness of our new model against the drastic appearance variations due to long-term visual changes, we formulate our objective to use non-squared ℓ2-norm distances, which leads to a difficult optimization problem that minimizes the ratio of the ℓ2,1-norms of matrices. To solve our objective, we derive a new efficient iterative algorithm, whose convergence is rigorously guaranteed by theory. In addition, because our solution is strictly orthogonal, the learned location representations can have better place recognition capabilities. We evaluate the proposed method using two large-scale benchmark data sets, the CMU-VL and Nordland data sets. Experimental results have validated the effectiveness of our new method in long-term visual place recognition applications.