An Efficient and Continuous Representation for Occupancy Mapping with Random Mapping
文献类型：会议论文
| 作者 | Liu X(刘旭)2,3,4; Li DC(李德才)2,3 ; He YQ(何玉庆)1,2,3
|
| 出版日期 | 2021 |
| 会议日期 | September 27 - October 1, 2021 |
| 会议地点 | Prague, Czech republic |
| 页码 | 6664-6671 |
| 英文摘要 | Generating meaningful spatial models of physical environments is a crucial ability for autonomous navigation of mobile robots. This paper considers the problem of building continuous occupancy maps from sparse and noisy sensor data. To this end, we propose a new method named random mapping maps that advances the popular methods in two aspects. Firstly, it can represent environment models in a memory-saving and time-saving manner by randomly mapping a low-dimensional feature space to a high-dimensional one where a linear model is learnt. Secondly, it can rapidly obtain accurate inferences of the occupancy states of the spatial locations. This technique is based on the random mapping that projects the measurement data into a random feature space in which a discriminative model is learnt by the available data. It can asymptotically represent the complexity of the real world as the mapping dimension increases. Evaluations of the proposed method were conducted on various environments to verify its availability to environment modeling. Its performances in terms of time and memory consumptions were evaluated quantitatively. Finally, as a practical application, experiments about path planning were conducted based on the gradients of the proposed representation of environment model. |
| 产权排序 | 1 |
| 会议录 | IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2021
 |
| 会议录出版者 | IEEE |
| 会议录出版地 | New York |
| 语种 | 英语 |
| ISSN号 | 2153-0858 |
| ISBN号 | 978-1-6654-1714-3 |
| WOS记录号 | WOS:000755125505049 |
| 源URL | [http://ir.sia.cn/handle/173321/30494]  |
| 专题 | 沈阳自动化研究所_机器人学研究室 |
| 通讯作者 | Liu X(刘旭) |
| 作者单位 | 1.Shenyang Institute of Automation (Guangzhou), Chinese Academy of Sciences, Guangzhou 511458, China 2.State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, China 3.Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang 110016, China 4.University of Chinese Academy of Sciences, Beijing 100049, China |
| 推荐引用方式 GB/T 7714 | Liu X,Li DC,He YQ. An Efficient and Continuous Representation for Occupancy Mapping with Random Mapping[C]. 见:. Prague, Czech republic. September 27 - October 1, 2021. |
入库方式： OAI收割
来源：沈阳自动化研究所
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