机器人同时定位与建图方法研究
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摘要
移动机器人的同时定位与建图(Simultaneous Localization and Mapping, SLAM)作为机器人系统中的一个重要分支,是保证机器人在未知环境探索中是否能完全自主的关键所在。
本论文围绕机器人SLAM中的一些关键问题,深入研究了机器人的地图创建、地图融合以及多机器人之间的相互定位等问题,提出了一些自己的看法和解决方案,主要体现在以下几个方面：
(1)在详细分析声纳传感器的镜面反射和散射特性在测距过程中造成的一些不确定信息的基础上,提出了基于不确定性信息的概率栅格地图和特征几何地图的创建方法。在概率栅格地图中,引入了距离影响因子,调整了声纳概率测距模型,提高了地图的精度。在特征几何地图中,结合随机Hough变换(Radomized Hough Transform, RHT)和多分辨率Hough变换(Multi-resolution Hough Transform, MHT)算法的优点,提出了一种快速的Hough变换提取特征的方法。该方法将所有声纳弧上的离散点作为种子点,然后随机选取一部分离散点与种子点匹配,匹配的话累积存储单元就加一,当累积存储单元具有最大累积数时,则提取出直线特征。在一定程度上去除了一些不确定信息,从而对一些镜面反射或干扰噪声所造成的错误信息具有一定的鲁棒性。
(2)采用分散探索、集中建图的混合式控制结构,提出了基于差异进化算法的地图融合方法。首先让机器人从各自不同位置出发,融合各类传感器数据创建栅格局部地图；然后通过改进的差异进化(Differential Evolution, DE)算法搜索最优转换函数,根据转换函数旋转和平移某个地图,使得该地图和其它地图之间的重叠区域最大,相异度最小；最后通过接受函数的值来判断是否成功融合为一个全局地图。其中搜索最优转换函数采用改进的差异进化算法,变异策略DE/best/1和DE/rand/1通过线性模拟退火加权策略组合成新的变异操作,用线性退火因子作为加权因子,提高算法的收敛精度和收敛速率,快速成功的完成局部地图的融合。同时,在基于Hough变换的地图融合方法的基础上,分析了重叠度、旋转角度对地图融合算法的性能影响,通过随机采样和固定步长对Hough变换的离散点进行采样,提高地图创建的实时性。
(3)深入分析了基于粒子滤波的SLAM方法,提出了基于粒子群优化的机器人SLAM方法。首先针对粒子存在的退化问题,将粒子群优化和FasSLAM方法相结合,在预测采样过程中结合机器人的观测值进行优化,从而增强了位置预测的准确性,有效的解决了粒子退化问题；然后针对粒子存在的耗尽问题,引入遗传算法中的变异操作,保持了粒子的多样性；最后将其粒子群优化的思想扩展到异质多机器人的FastSLAM算法中。在异质多机器人系统中,充分利用某个机器人的精确定位能力,测出与其它机器人之间的相对位姿(距离和角度),并将其相对的观测量融合到粒子的预测采样过程中,提高了机器人之间的相互定位精度。
(4)在基于稀疏扩展信息滤波的基础上,研究了稀疏规则和数据关联问题,提出了一种新的稀疏化规则。该稀疏规则将活跃集合和使之不活跃集合继续分解成显性和隐性两部分,结合下一时刻所观测的特征,调整机器人和特征之间的关联强度,从而使两集合之间的元素产生相应的变化,确保弱连接的删除,强连接的保留；在解决数据关联方面采用基于马氏距离的增量式数据关联方法,提高特征之间的匹配精度,进一步降低了地图融合过程的误差,实现了大规模环境中机器人的SLAM。
Simultaneous Localization and Mapping (SLAM), an important branch on the mobile robot system, is to ensure whether robot exploration can be completed independently in unknown environments.
Mapping, map merging and cooperative localization, the key issues in robot SLAM, have been studied deeply, and some solving schemes are suggested. The research work can be summarized in the following aspects:
(1) Based on detailed analyses of uncertainty sensor information processing in ultrasonic ranging, especially on the scattering and specular reflection, the novel methods of mapping building are presented. In probabilistic grid maps, range confidence factor is introduced in ultrasonic model, and improves the map accuracy. In feature maps, a novel method is advanced based on Randomized Hough Transform (RHT) and Multi-resolution Hough Transforms (MHT). The method regards all discrete points as seed points, and randomly picks a part of discrete points for pairing with seed points. Corresponding accumulator cells are incremented in the space. When the accumulator cells reach the maximum, the line is extracted. So uncertainty ultrasonic sensor responses can be successfully reduced and the robustness for measuring distance is proved.
(2) The methods of decentralized exploration and concentrated mapping are adopted in the robot map building system. Firstly, each robot starts from different positions and builds the local map; secondly, the improved differential evolution (DE) algorithm is used to search the transform functions; thirdly, according to the transform functions, the map is rotated and translated so that the overlapping region is maximum and the dissimilarity is minimum; finally, accepting function determines whether map merging is completed successfully. The improved different algorithms adopt a novel mutation which combines mutation strategy DE/best/1with DE/rand/1by a linear simulated annealing strategy, and linear annealing factors are used as weighting factors, which improve the convergence precision and convergence speed in local maps merging. To examine the Hough transform based map merging algorithms, we research on the algorithm's performance in a series of elemental tests, such as overlapping and rotation. We focus on the performance when only subsets of points are picked randomly or in a deterministic way to compute the transformation.
(3) Via a deep study on SLAM, this paper introduces a novel method of SLAM based on particle filters to solve particle impoverishment and particle depletion. The solution to the first problem is to integrate particle swarm optimization (PSO) with FastSLAM. Through the particle swarm optimization, the predictions of particles are updated, and the particle's proposal distribution is adjusted, so the accuracy of position prediction is enhanced. To solve the second problem, the mutation operation based on genetic algorithm is adopted in PSO, so as to keep the particle diversity. Then, the PSO is applied to FastSLAM algorithm for heterogeneous multi-robot. In heterogeneous multi-robot system, we make full use of the robot ability of accuracy localization. The relative locations (including distance and orientation) are calculated between the robots, so the predictions of particles are updated considering the relative observations. By so doing the precision of localization is improved.
(4) Based on sparse extended information filter (SEIF), a new sparse rule is proposed. In the sparse rules, active sets and the sets with active features that we seek to deactivate are decomposed into two parts:the overt and covert. Then the associations between the robot and the features are adjusted according to the observations of the next moment, and the elements of two sets are changed corresponding, thus ensuring the removal of a weak connection and retain a strong connection. As to the questions of data association, incremental data association methods based on Mahalanobis distance are adopted, which have improved the matching accuracy, reduced the error data of map merging, and achieved robot SLAM in a large-scale environment.
本论文围绕机器人SLAM中的一些关键问题,深入研究了机器人的地图创建、地图融合以及多机器人之间的相互定位等问题,提出了一些自己的看法和解决方案,主要体现在以下几个方面：
(1)在详细分析声纳传感器的镜面反射和散射特性在测距过程中造成的一些不确定信息的基础上,提出了基于不确定性信息的概率栅格地图和特征几何地图的创建方法。在概率栅格地图中,引入了距离影响因子,调整了声纳概率测距模型,提高了地图的精度。在特征几何地图中,结合随机Hough变换(Radomized Hough Transform, RHT)和多分辨率Hough变换(Multi-resolution Hough Transform, MHT)算法的优点,提出了一种快速的Hough变换提取特征的方法。该方法将所有声纳弧上的离散点作为种子点,然后随机选取一部分离散点与种子点匹配,匹配的话累积存储单元就加一,当累积存储单元具有最大累积数时,则提取出直线特征。在一定程度上去除了一些不确定信息,从而对一些镜面反射或干扰噪声所造成的错误信息具有一定的鲁棒性。
(2)采用分散探索、集中建图的混合式控制结构,提出了基于差异进化算法的地图融合方法。首先让机器人从各自不同位置出发,融合各类传感器数据创建栅格局部地图；然后通过改进的差异进化(Differential Evolution, DE)算法搜索最优转换函数,根据转换函数旋转和平移某个地图,使得该地图和其它地图之间的重叠区域最大,相异度最小；最后通过接受函数的值来判断是否成功融合为一个全局地图。其中搜索最优转换函数采用改进的差异进化算法,变异策略DE/best/1和DE/rand/1通过线性模拟退火加权策略组合成新的变异操作,用线性退火因子作为加权因子,提高算法的收敛精度和收敛速率,快速成功的完成局部地图的融合。同时,在基于Hough变换的地图融合方法的基础上,分析了重叠度、旋转角度对地图融合算法的性能影响,通过随机采样和固定步长对Hough变换的离散点进行采样,提高地图创建的实时性。
(3)深入分析了基于粒子滤波的SLAM方法,提出了基于粒子群优化的机器人SLAM方法。首先针对粒子存在的退化问题,将粒子群优化和FasSLAM方法相结合,在预测采样过程中结合机器人的观测值进行优化,从而增强了位置预测的准确性,有效的解决了粒子退化问题；然后针对粒子存在的耗尽问题,引入遗传算法中的变异操作,保持了粒子的多样性；最后将其粒子群优化的思想扩展到异质多机器人的FastSLAM算法中。在异质多机器人系统中,充分利用某个机器人的精确定位能力,测出与其它机器人之间的相对位姿(距离和角度),并将其相对的观测量融合到粒子的预测采样过程中,提高了机器人之间的相互定位精度。
(4)在基于稀疏扩展信息滤波的基础上,研究了稀疏规则和数据关联问题,提出了一种新的稀疏化规则。该稀疏规则将活跃集合和使之不活跃集合继续分解成显性和隐性两部分,结合下一时刻所观测的特征,调整机器人和特征之间的关联强度,从而使两集合之间的元素产生相应的变化,确保弱连接的删除,强连接的保留；在解决数据关联方面采用基于马氏距离的增量式数据关联方法,提高特征之间的匹配精度,进一步降低了地图融合过程的误差,实现了大规模环境中机器人的SLAM。
Simultaneous Localization and Mapping (SLAM), an important branch on the mobile robot system, is to ensure whether robot exploration can be completed independently in unknown environments.
Mapping, map merging and cooperative localization, the key issues in robot SLAM, have been studied deeply, and some solving schemes are suggested. The research work can be summarized in the following aspects:
(1) Based on detailed analyses of uncertainty sensor information processing in ultrasonic ranging, especially on the scattering and specular reflection, the novel methods of mapping building are presented. In probabilistic grid maps, range confidence factor is introduced in ultrasonic model, and improves the map accuracy. In feature maps, a novel method is advanced based on Randomized Hough Transform (RHT) and Multi-resolution Hough Transforms (MHT). The method regards all discrete points as seed points, and randomly picks a part of discrete points for pairing with seed points. Corresponding accumulator cells are incremented in the space. When the accumulator cells reach the maximum, the line is extracted. So uncertainty ultrasonic sensor responses can be successfully reduced and the robustness for measuring distance is proved.
(2) The methods of decentralized exploration and concentrated mapping are adopted in the robot map building system. Firstly, each robot starts from different positions and builds the local map; secondly, the improved differential evolution (DE) algorithm is used to search the transform functions; thirdly, according to the transform functions, the map is rotated and translated so that the overlapping region is maximum and the dissimilarity is minimum; finally, accepting function determines whether map merging is completed successfully. The improved different algorithms adopt a novel mutation which combines mutation strategy DE/best/1with DE/rand/1by a linear simulated annealing strategy, and linear annealing factors are used as weighting factors, which improve the convergence precision and convergence speed in local maps merging. To examine the Hough transform based map merging algorithms, we research on the algorithm's performance in a series of elemental tests, such as overlapping and rotation. We focus on the performance when only subsets of points are picked randomly or in a deterministic way to compute the transformation.
(3) Via a deep study on SLAM, this paper introduces a novel method of SLAM based on particle filters to solve particle impoverishment and particle depletion. The solution to the first problem is to integrate particle swarm optimization (PSO) with FastSLAM. Through the particle swarm optimization, the predictions of particles are updated, and the particle's proposal distribution is adjusted, so the accuracy of position prediction is enhanced. To solve the second problem, the mutation operation based on genetic algorithm is adopted in PSO, so as to keep the particle diversity. Then, the PSO is applied to FastSLAM algorithm for heterogeneous multi-robot. In heterogeneous multi-robot system, we make full use of the robot ability of accuracy localization. The relative locations (including distance and orientation) are calculated between the robots, so the predictions of particles are updated considering the relative observations. By so doing the precision of localization is improved.
(4) Based on sparse extended information filter (SEIF), a new sparse rule is proposed. In the sparse rules, active sets and the sets with active features that we seek to deactivate are decomposed into two parts:the overt and covert. Then the associations between the robot and the features are adjusted according to the observations of the next moment, and the elements of two sets are changed corresponding, thus ensuring the removal of a weak connection and retain a strong connection. As to the questions of data association, incremental data association methods based on Mahalanobis distance are adopted, which have improved the matching accuracy, reduced the error data of map merging, and achieved robot SLAM in a large-scale environment.
引文
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