长期运行移动机器人的定位与地图构建

发布时间：2018-01-07 04:17

  本文关键词：长期运行移动机器人的定位与地图构建　出处：《浙江大学》2016年博士论文　论文类型：学位论文

  更多相关文章： 同时定位与地图构建 长期运行 动态环境 概率模型

【摘要】：移动机器人领域的发展有效提升了机器人的自主性,使机器人具备更大的工作范围,更低的环境部署要求,以及更灵活丰富的任务执行等,对于机器人的进一步应用推广有重要的意义。在移动机器人技术方面,最首要的任务是回答“我在哪里”,即定位问题。目前,最常见的方式是机器人先对环境进行地图构建,然后利用构建的地图进行定位。其中,地图构建依赖同时定位与构图(SLAM)系统。然而,这种方式对于长期运行案例,比如仓储机器人、巡检机器人等不再可行。因为SLAM得到的地图在定位期间可能已经过期,甚至SLAM期间就已经过期。针对这个问题,本文提出一种支持机器人长期运行的多阶段SLAM方法,思路是通过将长期运行SLAM问题转化为多阶段SLAM问题,假设阶段内环境静止,而阶段间环境变化,从而给出一种多阶段SLAM方案,使得移动机器人能够适应环境变化,构建具有时效性的地图,最终具有长期定位能力,并且计算复杂度可接受。具体包括了三方面五个创新点的内容：(1)信息层面从冗余到精简,本文从两张地图之间的Kullback-Leibler距离出发,导出了衡量构建两张地图的位姿集合间的量化差距,基于这个差距提出了图模型位姿节点的修剪算法。在此之上,本文又提出位姿修剪后,在图模型中生成新的稀疏因子的方法,保留一部分节点用于维持图模型几何关系的信息。实现通过控制节点使图模型保持与地图相关的复杂度,而非原来的轨迹长度；(2)观测层面从静态到动态,本文从位姿估计出发,利用概率模型将问题转化为传感器类型无关的一般化概率推断和参数估计问题,并发现该框架能够囊括许多经典算法,并且借助该模型提出了多传感器的融合框架。在此一般化模型的基础上,本文又将环境的动态融合到模型的变量中,使动态环境检测变为多传感器下的概率模型推理问题,提升了计算效率和准确率。(3)框架层面从单次到多次,本文通过将单次SLAM转化为多次SLAM,并在每次SLAM之间设立包含基于修剪的冗余性处理和基于动态检测的时效性处理的图模型控制模块,使SLAM可以长期运行,仅通过损失少量精度,就可以获得地图有界前提下的常数复杂度。最终,通过在包含传感器,区域,环境类型等多个变量的多个数据集上实验,证明了所提出的系统的可行性及误差非累积特性,为长期运行机器人的SLAM问题提供了初步的理论和实践结论。
[Abstract]:The development in the field of mobile robot can effectively enhance the autonomy of the robot, the robot has a larger working range, lower environment deployment requirements, more flexible and rich implementation, has an important significance for further application of the robot. The mobile robot technology, the most important task is to answer "I where, namely location problem. At present, the most common way is to first robot environment map building, and then use the built map positioning. The map is built upon simultaneous localization and mapping (SLAM) system. However, this way for a long running case, such as warehousing robot inspection robot is no longer feasible. Because the SLAM map may have expired during location, even during the period of SLAM has expired. To solve this problem, this paper suggests that the long-term operation of a human machine support Multi stage SLAM method, the ideas through the long run of SLAM problem is transformed into a multi stage SLAM environment problem, stationary hypothesis stage, and the stage of environmental change, which is a multi stage SLAM scheme, so that the mobile robot can adapt to the change of the environment, build a map of the final effect, with long-term capacity, complex the degree of acceptance and calculation. Including three aspects five innovation contents: (1) from the aspect of information redundancy to streamline, the map distance between two Kullback-Leibler of derived measure was constructed to quantify gap pose two maps between the sets, the pruning algorithm of graph nodes pose model based on the gap. On this basis, this paper proposes the pose after pruning method to generate a new factor in sparse graph model, some nodes reserved for maintaining the geometric relations of letter graph model Information. Through the control nodes to maintain graph model and map related complexity, rather than the original track length; (2) the observation level from static to dynamic, from the pose estimation of the problem is transformed into a general probabilistic sensor type independent inference and parameter estimation problem using the probabilistic model, and found that the framework can include many classic algorithms, and with the help of the model proposed. Multi sensor fusion framework based on this general model, this paper will environment dynamic fusion to the model variables, the dynamic environment detection to the probability model of reasoning problems under multi sensor, improve the computation efficiency and accuracy. (3) the framework level from single to multiple, the single SLAM into multiple SLAM, and in every SLAM set up based on inclusion redundancy processing and pruning based on timeliness of dynamic detection The graph model control module, so that SLAM can run only by a small amount of long-term, loss of accuracy, you can get the map with a constant circle under the premise of complexity. Finally, through the included sensor, area, multi variable environment types on the dataset experiment, feasibility and error of the proposed system the non accumulation characteristics, provides a theoretical and practical conclusions SLAM problem for the long-term operation of the robot.

【学位授予单位】：浙江大学
【学位级别】：博士
【学位授予年份】：2016
【分类号】：TP242
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本文编号：1390948