Sequence-based sparse optimization methods for long-term loop closure detection in visual SLAM
Sequence-based sparse optimization methods for long-term loop closure detection in visual SLAM
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DOI：
10.1007/s10514-018-9736-3
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发表时间：
2018-04
影响因子：
3.5
通讯作者：
Fei Han;Hua Wang;G. Huang;Hao Zhang
中科院分区：
文献类型：
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作者：
Fei Han;Hua Wang;G. Huang;Hao Zhang
环闭合检测是同步定位与地图构建（SLAM）中最重要的模块之一，因为它能够发现不同地点之间的全局拓扑。当当前地点被识别为与先前访问的地点匹配时，检测到循环闭合。当SLAM在整个长期周期内执行时，环路闭合检测将存在额外的挑战。照明、天气和植被条件在终身SLAM期间通常会显著变化，从而导致环路闭合检测中的严重感知混叠和外观变化问题。为了解决这个问题，我们提出了一种新的鲁棒多模态序列（ROMS）的方法，用于长期视觉SLAM中的鲁棒环路闭合检测。在我们的ROMS方法中，图像序列被用作地方的表示，其中序列中的每个图像由多个特征模态编码，以便可以区分地识别不同的地方。我们制定了强大的地方识别问题作为一个凸优化问题与结构稀疏正则化，由于事实上，只有一小部分模板的地方可以匹配的查询的地方。此外，我们还开发了一个新的算法来有效地解决公式化的优化问题，这保证了收敛到全局最优理论。我们的ROMS方法是通过对三个大规模基准数据集进行广泛的实验来评估的，这些数据集记录了一天中不同时间、月份和季节的场景。实验结果表明，我们的ROMS方法优于现有的环路闭合检测方法在长期的SLAM，并达到了最先进的性能。
Loop closure detection is one of the most important module in Simultaneously Localization and Mapping (SLAM) because it enables to find the global topology among different places. A loop closure is detected when the current place is recognized to match the previous visited places. When the SLAM is executed throughout a long-term period, there will be additional challenges for the loop closure detection. The illumination, weather, and vegetation conditions can often change significantly during the life-long SLAM, resulting in the critical strong perceptual aliasing and appearance variation problems in loop closure detection. In order to address this problem, we propose a new Robust Multimodal Sequence-based (ROMS) method for robust loop closure detection in long-term visual SLAM. A sequence of images is used as the representation of places in our ROMS method, where each image in the sequence is encoded by multiple feature modalites so that different places can be recognized discriminatively. We formulate the robust place recognition problem as a convex optimization problem with structured sparsity regularization due to the fact that only a small set of template places can match the query place. In addition, we also develop a new algorithm to solve the formulated optimization problem efficiently, which guarantees to converge to the global optima theoretically. Our ROMS method is evaluated through extensive experiments on three large-scale benchmark datasets, which record scenes ranging from different times of the day, months, and seasons. Experimental results demonstrate that our ROMS method outperforms the existing loop closure detection methods in long-term SLAM, and achieves the state-of-the-art performance.