自主式水下航行器同步定位与地图构建算法研究
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摘要
自主式水下航行器(AUV)在军事、科考和工程领域具有巨大的潜在价值和商业化前景，已成为机器人领域的一个热点问题。导航技术是决定AUV发展水平的关键技术之一，随着AUV向远程化和深海化发展，传统的导航方式已无法适应AUV快速发展的要求。同步定位与地图构建(SLAM)因其无需先验环境地图以及外部传感器结构简单等优势，对于提高AUV导航自主性具有重大的现实意义。本文针对AUV的SLAM问题，对其中的关键算法和技术问题展开研究。
论文的研究工作和成果主要包括：
首先对AUV的运动控制模型、环境地图模型、传感器观测模型等进行了讨论，并对这些模型进行了合理简化，接着给出AUV定位问题、地图构建问题的描述，在此基础上建立SLAM问题的概率模型及算法性能评价标准。
对于小范围环境下的AUV同步定位与地图构建问题，EKF-SLAM具有算法结构简单、数学理论严谨等优点，使用UKF代替EKF能克服EKF-SLAM算法处理非线性问题时引入线性化误差的问题，但在实际应用中UKF的估计精度依赖于准确的先验噪声模型，缺乏自适应调节能力。针对这个问题，提出了一种鲁棒UKF-SLAM算法，通过使用一个多维观测噪声尺度因子对实际噪声统计特性进行跟踪，使算法在先验噪声模型未知或其统计特性时变情况下仍然保持较高的估计精度和稳定性。与EKF-SLAM和UKF-SLAM的对比仿真验证了算法的有效性。
对于大范围或特征密集环境下的AUV同步定位与地图构建问题，基于粒子滤波(PF)的FastSLAM算法是一种常用的解决方案。针对传统PF算法不具备处理非高斯非稳态噪声的能力，提出了一种基于极大似然估计的PF算法，利用一系列独立的加权高斯噪声序列对真实噪声进行逼近，并将PF算法的粒子重要性权值转化为每个高斯噪声序列对应的概率密度函数，然后通过极大似然估计计算出这些独立噪声序列的分布参数及其权值，最终确定粒子重要性权值。将这种改进的PF算法用于FastSLAM框架，实现了非稳态非高斯噪声下AUV的同步定位与地图构建，仿真实验验证了算法的有效性。
数据关联是SLAM的核心问题之一，直接影响到AUV状态和环境地图估计的精度。在对常见数据关联方法原理及特点进行分析比较的基础上，提出了一种基于模糊逻辑的数据关联方法。该方法以特征状态估计和特征观测的误差椭圆为对象，从中挖掘反映特征观测和特征估计的相关性信息并投影到论域上的模糊集合，通过建立一定的模糊规则，对多种特征信息进行融合推理，将模糊输出变量作为关联结果。仿真实验表明模糊数据关联算法具有比传统算法更高的关联正确率和更强的抗干扰能力。
传统SLAM模型利用向量序列形式表达空间信息，对于杂波环境中的AUV导航问题，这种SLAM模型无法有效表达杂波对观测带来的漏检、传感器虚警以及观测不确定性等多种信息，算法的性能受到较大影响。针对这个问题，提出了一种基于随机有限集(RFS)的SLAM模型，将SLAM问题中的状态信息、观测信息以及环境地图都表示成RFS形式，在Bayes估计框架下利用概率假设密度(PHD)滤波进行联合目标状态估计，并通过粒子滤波对PHD滤波进行实现。此外，在由粒子集获取目标状态时，为克服传统峰值提取算法缺陷，提出了一种粒子集时滞输出的目标状态提取方法。仿真实验表明，在杂波环境中基于RFS的SLAM算法相比传统SLAM算法具有更高的估计精度和稳定性。
Autonomous underwater vehicle (AUV) has tremendous potential value and commercialprospects in the military, scientific and engineering fields, and it has become a hot issue in thefield of robotic research. Navigation is one of the key technologies which determine thedevelopment of AUV, and with the current of AUV toward the long-range and deep-sea thetraditional navigation methods are unable to satisfy the requirements of fast developing.Simultaneous localization and mapping (SLAM) is of great practical significance forimproving the autonomy of AUV Navigation because of its simple structure of externalsensor and no need for a priori environment map. In this paper, some key algorithms andtechnologies of SLAM for AUV are studied.
The main research contents and achievements of this paper are as follows:
Firstly, the motion control model, environment model and sensor observe model of AUVare discussed and simplified reasonably. And then, the description of AUV’s localizationproblem and mapping problem are proposed. On this basis, the probabilistic model of SLAMproblem and criteria of algorithm performance are established.
EKF-SLAM algorithm has simple structure and rigorous mathematical theory for AUV’sSLAM problem in small-scale environment. Using UKF instead of EKF can eliminate thelinearization error introduced by EKF-SLAM in processing non-linear problem. But UKF islack of adaptive ability in practical application, because its estimate accuracy depends onprecise prior noise model. To solve this problem, a robust UKF-SLAM algorithm is proposed,which tracks actual noise statistical characteristics by using a multi-dimensional observenoise scale factor. As a result, it keeps high estimate accuracy and stability when the priornoise is unknown or the noise statistical characteristics are time-varying. The comparativesimulations between EKF-SLAM, UKF-SLAM and robust UKF-SLAM prove theeffectiveness of the new algorithm.
For large-scale or dense-feature environment, the FastSLAM algorithm based on particlefilter (PF) is suitable for AUV SLAM problem. An improved particle filter based onmaximum likelihood estimation is proposed to solve the problem that traditional PFalgorithm can not obtain the estimation of a nonlinear system with the assumption ofnon-Gaussian non-stationary noise. In the improved PF algorithm, the real noise distributionis approximated by a series of independent weighted Gauusian noise sequences, and theimportance weights of PF are transformed to probability density function of each Gauusian noise sequence. Then the distribution parameters and weights of these noise sequences can becaculated by maximum likelihood estimation, and thus the weights of PF algorithm can bedetermined. By using maximum likelihood based PF algorithm in FastSLAM framework, theSLAM of AUV with non-Gaussian non-stationary noise is implemented. At last, theeffectiveness of proposed algorithm is verified by simulations.
Data association is one of the core problems of SLAM, which directly affects theaccuracy of AUV's localization and environmental map estimation. A data associationalgorithm based on fuzzy logic is proposed on the basis of analyzing and comparing principleand characteristic of common data association methods. In this algorithm, the relatedinformation between feature measurement and feature estimation is mined from their ellipses,and projected into the fuzzy set on the domain. A variety of information is fusioned andreasoned by establishing certain fuzzy rules, and the data association result is just the fuzzyoutput variable. The simulations indicate that the fuzzy data association has higherassociation accuracy and stronger anti-disturbance ability compared with traditionalalgorithms.
In traditional feature based SLAM model, the space information is described by vectorsequence. As a result, this kind of model can not effectively express multiple observeinformation such as loss detecting, false alarm and observe uncertainty etc. for AUVnavigation problem in the cluttered environment, and the algorithm performance is severelyaffected. To solve this problem, a SLAM model based on random finite set (RFS) theory isproposed. In the novel model, the system state, observation and environment map in SLAMare all represented in the form of RFS. The estimation of joint target state variable is carriedout through probability hypothesis density (PHD) filter in the Bayes estimate framework, andthe PHD filter is realized by particle filter. Additionally, a target state extracting method basedon particle set time-delay outputting is putted forward to overcome the defaults of traditionalextracting algorithm. Simulations show that in the cluttered environment the RFS-SLAM canobtain higher estimate accuracy and stability compared with traditional SLAM model.
论文的研究工作和成果主要包括：
首先对AUV的运动控制模型、环境地图模型、传感器观测模型等进行了讨论，并对这些模型进行了合理简化，接着给出AUV定位问题、地图构建问题的描述，在此基础上建立SLAM问题的概率模型及算法性能评价标准。
对于小范围环境下的AUV同步定位与地图构建问题，EKF-SLAM具有算法结构简单、数学理论严谨等优点，使用UKF代替EKF能克服EKF-SLAM算法处理非线性问题时引入线性化误差的问题，但在实际应用中UKF的估计精度依赖于准确的先验噪声模型，缺乏自适应调节能力。针对这个问题，提出了一种鲁棒UKF-SLAM算法，通过使用一个多维观测噪声尺度因子对实际噪声统计特性进行跟踪，使算法在先验噪声模型未知或其统计特性时变情况下仍然保持较高的估计精度和稳定性。与EKF-SLAM和UKF-SLAM的对比仿真验证了算法的有效性。
对于大范围或特征密集环境下的AUV同步定位与地图构建问题，基于粒子滤波(PF)的FastSLAM算法是一种常用的解决方案。针对传统PF算法不具备处理非高斯非稳态噪声的能力，提出了一种基于极大似然估计的PF算法，利用一系列独立的加权高斯噪声序列对真实噪声进行逼近，并将PF算法的粒子重要性权值转化为每个高斯噪声序列对应的概率密度函数，然后通过极大似然估计计算出这些独立噪声序列的分布参数及其权值，最终确定粒子重要性权值。将这种改进的PF算法用于FastSLAM框架，实现了非稳态非高斯噪声下AUV的同步定位与地图构建，仿真实验验证了算法的有效性。
数据关联是SLAM的核心问题之一，直接影响到AUV状态和环境地图估计的精度。在对常见数据关联方法原理及特点进行分析比较的基础上，提出了一种基于模糊逻辑的数据关联方法。该方法以特征状态估计和特征观测的误差椭圆为对象，从中挖掘反映特征观测和特征估计的相关性信息并投影到论域上的模糊集合，通过建立一定的模糊规则，对多种特征信息进行融合推理，将模糊输出变量作为关联结果。仿真实验表明模糊数据关联算法具有比传统算法更高的关联正确率和更强的抗干扰能力。
传统SLAM模型利用向量序列形式表达空间信息，对于杂波环境中的AUV导航问题，这种SLAM模型无法有效表达杂波对观测带来的漏检、传感器虚警以及观测不确定性等多种信息，算法的性能受到较大影响。针对这个问题，提出了一种基于随机有限集(RFS)的SLAM模型，将SLAM问题中的状态信息、观测信息以及环境地图都表示成RFS形式，在Bayes估计框架下利用概率假设密度(PHD)滤波进行联合目标状态估计，并通过粒子滤波对PHD滤波进行实现。此外，在由粒子集获取目标状态时，为克服传统峰值提取算法缺陷，提出了一种粒子集时滞输出的目标状态提取方法。仿真实验表明，在杂波环境中基于RFS的SLAM算法相比传统SLAM算法具有更高的估计精度和稳定性。
Autonomous underwater vehicle (AUV) has tremendous potential value and commercialprospects in the military, scientific and engineering fields, and it has become a hot issue in thefield of robotic research. Navigation is one of the key technologies which determine thedevelopment of AUV, and with the current of AUV toward the long-range and deep-sea thetraditional navigation methods are unable to satisfy the requirements of fast developing.Simultaneous localization and mapping (SLAM) is of great practical significance forimproving the autonomy of AUV Navigation because of its simple structure of externalsensor and no need for a priori environment map. In this paper, some key algorithms andtechnologies of SLAM for AUV are studied.
The main research contents and achievements of this paper are as follows:
Firstly, the motion control model, environment model and sensor observe model of AUVare discussed and simplified reasonably. And then, the description of AUV’s localizationproblem and mapping problem are proposed. On this basis, the probabilistic model of SLAMproblem and criteria of algorithm performance are established.
EKF-SLAM algorithm has simple structure and rigorous mathematical theory for AUV’sSLAM problem in small-scale environment. Using UKF instead of EKF can eliminate thelinearization error introduced by EKF-SLAM in processing non-linear problem. But UKF islack of adaptive ability in practical application, because its estimate accuracy depends onprecise prior noise model. To solve this problem, a robust UKF-SLAM algorithm is proposed,which tracks actual noise statistical characteristics by using a multi-dimensional observenoise scale factor. As a result, it keeps high estimate accuracy and stability when the priornoise is unknown or the noise statistical characteristics are time-varying. The comparativesimulations between EKF-SLAM, UKF-SLAM and robust UKF-SLAM prove theeffectiveness of the new algorithm.
For large-scale or dense-feature environment, the FastSLAM algorithm based on particlefilter (PF) is suitable for AUV SLAM problem. An improved particle filter based onmaximum likelihood estimation is proposed to solve the problem that traditional PFalgorithm can not obtain the estimation of a nonlinear system with the assumption ofnon-Gaussian non-stationary noise. In the improved PF algorithm, the real noise distributionis approximated by a series of independent weighted Gauusian noise sequences, and theimportance weights of PF are transformed to probability density function of each Gauusian noise sequence. Then the distribution parameters and weights of these noise sequences can becaculated by maximum likelihood estimation, and thus the weights of PF algorithm can bedetermined. By using maximum likelihood based PF algorithm in FastSLAM framework, theSLAM of AUV with non-Gaussian non-stationary noise is implemented. At last, theeffectiveness of proposed algorithm is verified by simulations.
Data association is one of the core problems of SLAM, which directly affects theaccuracy of AUV's localization and environmental map estimation. A data associationalgorithm based on fuzzy logic is proposed on the basis of analyzing and comparing principleand characteristic of common data association methods. In this algorithm, the relatedinformation between feature measurement and feature estimation is mined from their ellipses,and projected into the fuzzy set on the domain. A variety of information is fusioned andreasoned by establishing certain fuzzy rules, and the data association result is just the fuzzyoutput variable. The simulations indicate that the fuzzy data association has higherassociation accuracy and stronger anti-disturbance ability compared with traditionalalgorithms.
In traditional feature based SLAM model, the space information is described by vectorsequence. As a result, this kind of model can not effectively express multiple observeinformation such as loss detecting, false alarm and observe uncertainty etc. for AUVnavigation problem in the cluttered environment, and the algorithm performance is severelyaffected. To solve this problem, a SLAM model based on random finite set (RFS) theory isproposed. In the novel model, the system state, observation and environment map in SLAMare all represented in the form of RFS. The estimation of joint target state variable is carriedout through probability hypothesis density (PHD) filter in the Bayes estimate framework, andthe PHD filter is realized by particle filter. Additionally, a target state extracting method basedon particle set time-delay outputting is putted forward to overcome the defaults of traditionalextracting algorithm. Simulations show that in the cluttered environment the RFS-SLAM canobtain higher estimate accuracy and stability compared with traditional SLAM model.
引文
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