Sharing Heterogeneous Spatial Knowledge: Map Fusion Between Asynchronous Monocular Vision and Lidar or Other Prior Inputs
Sharing Heterogeneous Spatial Knowledge: Map Fusion Between Asynchronous Monocular Vision and Lidar or Other Prior Inputs
复制标题
共享异构空间知识：异步单目视觉与激光雷达或其他先验输入之间的地图融合
DOI：
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发表时间：
2019
期刊：
影响因子：
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通讯作者：
Dezhen Song
中科院分区：
文献类型：
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作者：
Yan Lu;Joseph Lee;Shu;Hsin;Baifan Chen;Dezhen Song
为了使低成本的移动的设备和机器人配备了单目摄像头，以获得准确的位置信息，在GPS拒绝的环境中，我们建议使用预先收集的激光雷达或其他先验数据，以纠正不精确的视觉同步定位和映射（SLAM）的结果。这导致了一个新的和不平凡的问题，融合视觉和以前/激光雷达数据在不同的角度和时间。事实上，激光雷达输入可以被其他先前的映射输入替换，只要我们可以从这些输入中提取垂直平面。因此，它们通常被称为先验/激光雷达数据。我们利用从视觉和先验/激光雷达数据中提取的平面结构，并将其用作锚定信息来融合异构地图。我们制定了一个有约束的全球光束法平差共面约束和解决它使用的惩罚障碍的方法。通过误差分析，我们证明了共面约束有助于减少估计的不确定性。我们已经实现了该系统，并测试了它与真实的数据。初步结果表明，我们的算法显着减少了高达68.3%的视觉SLAM的绝对轨迹误差。
To enable low-cost mobile devices and robots equipped with monocular cameras to obtain accurate position information in GPS-denied environments, we propose to use pre-collected lidar or other prior data to rectify imprecise visual simultaneous localization and mapping (SLAM) results. This leads to a novel and nontrivial problem that fuses vision and prior/lidar data acquired at different perspectives and time. In fact, the lidar inputs can be replaced by other prior mapping inputs as long as we can extract vertical planes from these inputs. Hence, they are referred as prior/lidar data in general. We exploit the planar structure extracted from both vision and prior/lidar data and use it as the anchoring information to fuse the heterogeneous maps. We formulate a constrained global bundle adjustment using coplanarity constraints and solve it using a penalty-barrier approach. By error analysis we prove that the coplanarity constraints help reduce the estimation uncertainties. We have implemented the system and tested it with real data. The initial results show that our algorithm significantly reduces the absolute trajectory error of visual SLAM by as much as 68.3%.
影响因子：
7.8
作者：
Endres, Felix;Hess, Juergen;Burgard, Wolfram
通讯作者：
Burgard, Wolfram