Topological local-metric framework for mobile robots navigation: a long term perspective
Topological local-metric framework for mobile robots navigation: a long term perspective
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移动机器人导航的拓扑局部度量框架：长期视角
DOI：
10.1007/s10514-018-9724-7
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发表时间：
2018-03
影响因子：
3.5
通讯作者：
Shoudong Huang
中科院分区：
文献类型：
--
作者：
Li Tang;Yue Wang;Xiaqing Ding;Huan Yin;Rong Xiong;Shoudong Huang
长期映射和定位是移动机器人在实际应用部署中的主要组成部分，其鲁棒性和稳定性是其面临的关键挑战。本文引入了一种拓扑局部度量框架（TLF），旨在处理环境变化、错误测量和实现恒定的复杂性。TLF将机器人采集到的传感器数据组织成拓扑图，拓扑图的几何形状仅编码在边缘，即相邻节点之间的相对位姿，将全局一致性松弛为局部一致性。因此，由于误差被限制在局部，因此TLF对传感器信息匹配中不可避免的错误测量具有更强的鲁棒性。在TLF的基础上，由于没有全局坐标，我们进一步提出了在多个局部度量坐标之间切换的定位和导航算法。此外，由于不需要全局优化，提出了一种终身记忆机制，以恒定的复杂度记忆TLF中的环境变化。在实验中，利用对光照敏感的立体摄像机采集的21节数据对框架和算法进行了评估，并与目前最先进的全局一致框架进行了比较。结果表明，TLF定位精度与全局一致框架定位精度相近，鲁棒性更强，成本更低。由于记忆机制的存在，定位性能也可以从会话中得到提高。最后，配备了TLF，机器人在1公里的时段内自主导航。
Long term mapping and localization are the primary components for mobile robots in real world application deployment, of which the crucial challenge is the robustness and stability. In this paper, we introduce a topological local-metric framework (TLF), aiming at dealing with environmental changes, erroneous measurements and achieving constant complexity. TLF organizes the sensor data collected by the robot in a topological graph, of which the geometry is only encoded in the edge, i.e. the relative poses between adjacent nodes, relaxing the global consistency to local consistency. Therefore the TLF is more robust to unavoidable erroneous measurements from sensor information matching since the error is constrained in the local. Based on TLF, as there is no global coordinate, we further propose the localization and navigation algorithms by switching across multiple local metric coordinates. Besides, a lifelong memorizing mechanism is presented to memorize the environmental changes in the TLF with constant complexity, as no global optimization is required. In experiments, the framework and algorithms are evaluated on 21-session data collected by stereo cameras, which are sensitive to illumination, and compared with the state-of-art global consistent framework. The results demonstrate that TLF can achieve similar localization accuracy with that from global consistent framework, but brings higher robustness with lower cost. The localization performance can also be improved from sessions because of the memorizing mechanism. Finally, equipped with TLF, the robot navigates itself in a 1 km session autonomously.
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DOI：
10.1109/ecmr.2013.6698816
发表时间：
2013
期刊：
2013 European Conference on Mobile Robots
影响因子：
--
作者：
Yue Wang;R. Xiong;Qianshan Li;Shoudong Huang
通讯作者：
Yue Wang;R. Xiong;Qianshan Li;Shoudong Huang
DOI：
10.1109/robot.2009.5152501
发表时间：
2009-05
期刊：
2009 IEEE International Conference on Robotics and Automation
影响因子：
--
作者：
Adrien Angeli;S. Doncieux;Jean-Arcady Meyer;David Filliat
通讯作者：
Adrien Angeli;S. Doncieux;Jean-Arcady Meyer;David Filliat
DOI：
10.1109/icra.2014.6906961
发表时间：
2014-09
期刊：
2014 IEEE International Conference on Robotics and Automation (ICRA)
影响因子：
--
作者：
C. McManus;W. Churchill;William P. Maddern;Alexander D. Stewart;P. Newman
通讯作者：
C. McManus;W. Churchill;William P. Maddern;Alexander D. Stewart;P. Newman
DOI：
10.1109/34.121791
发表时间：
1992-02-01
影响因子：
23.6
作者：
BESL, PJ;MCKAY, ND
通讯作者：
MCKAY, ND
DOI：
10.1177/0278364910370376
发表时间：
2010-07
期刊：
The International Journal of Robotics Research
影响因子：
--
作者：
K. Konolige;James Bowman;Jindong Chen;P. Mihelich;Michael Calonder;V. Lepetit;P. Fua
通讯作者：
K. Konolige;James Bowman;Jindong Chen;P. Mihelich;Michael Calonder;V. Lepetit;P. Fua