LocNet: Global Localization in 3D Point Clouds for Mobile Vehicles
LocNet: Global Localization in 3D Point Clouds for Mobile Vehicles
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DOI：
10.1109/ivs.2018.8500682
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发表时间：
2017-12
期刊：
影响因子：
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通讯作者：
Huan Yin;Li Tang;X. Ding;Yue Wang;R. Xiong
中科院分区：
文献类型：
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作者：
Huan Yin;Li Tang;X. Ding;Yue Wang;R. Xiong
在没有任何先验知识的情况下，三维点云的全局定位是一个具有挑战性的问题。在本文中，提出了一种解决这个问题的方法，通过在全局先验映射中实现位置识别和度量姿态估计。具体来说，我们提出了一种半手工制作的表示学习方法，用于使用siamese LocNets的LiDAR点云，将位置识别问题描述为相似性建模问题。通过LocNet的最终学习表示，提出了一个仅具有范围观测的全局定位框架。为了证明我们的全球定位系统的性能和有效性，KITTI数据集被用来与其他算法进行比较，并在我们的长期多会话数据集进行评估。实验结果表明，该系统具有较高的精度。
Global localization in 3D point clouds is a challenging problem of estimating the pose of vehicles without any prior knowledge. In this paper, a solution to this problem is presented by achieving place recognition and metric pose estimation in the global prior map. Specifically, we present a semi-handcrafted representation learning method for LiDAR point clouds using siamese LocNets, which states the place recognition problem to a similarity modeling problem. With the final learned representations by LocNet, a global localization framework with range-only observations is proposed. To demonstrate the performance and effectiveness of our global localization system, KITTI dataset is employed for comparison with other algorithms, and also on our long-time multi-session datasets for evaluation. The result shows that our system can achieve high accuracy.