【作者】 钱钧；

【导师】 杨汝清；

【作者基本信息】 上海交通大学 ， 机械电子工程， 2009， 博士

【摘要】 同时定位与建图（Simultaneous Localization and Mapping, SLAM）是指移动机器人在探测未知环境时，为实现完全自治，建立增量式的环境地图、同时估计自身位姿的过程，是联合状态估计问题。本文使用激光雷达作为距离-角度测量传感器，感知室外环境中物体与机器人的相对位置，建立点特征地图；针对SLAM滤波器的一致性问题展开研究，并结合增量式地图创建过程的特点，提出若干改进方法，将其应用于智能车辆环境地图创建和侦察机器人自动回撤。在使用扩展卡尔曼滤波器（Extended Kalman Filter, EKF）解决SLAM问题时，利用完全协方差矩阵维护不同状态分量之间的互相关，在状态更新时利用相关性对不可观测的特征进行维护，但模型不精确和线性化引起的累积误差导致无法正确更新协方差矩阵，同时产生错误的状态估计，产生乐观估计。本文首先验证了机器人在运动前重复观测新的特征时，线性化误差必将导致不一致性，并发现机器人方向角的方差较大是主要因素。为了改善一致性能，提出了可移动坐标框架方法，在检测到新的特征时通过坐标变换，将参考框架移至机器人当前位姿处，使特征的初始方差仅由测量噪声决定，而与机器人位姿估计误差无关。状态分量之间的初始相关性较小、机器人方向角的卡尔曼增益矩阵较小以及位姿估计误差的有界性，为坐标框架移动提供了依据。改进的方法将未知环境探测变成始终在已探测环境中进行探测，因此将线性化误差引起的不一致性限制在局部环境中。由于EKF在维护协方差矩阵时具有平方复杂度，因此使用扩展信息滤波器（Extended Information Filter, EIF）解决SLAM问题。本文以图模型为数据结构，解释在坐标框架移动时，若保留机器人旧的位姿向量，则新的位姿不再与不可检测到的旧的特征之间产生约束，从而使图模型保持局部属性。为了同时获得较好的一致性和计算性能，提出了可移动坐标框架的稀疏信息滤波器，当机器人在不同局部环境中进行切换时，使用坐标框架移动和保留机器人位姿，使信息矩阵具有确切稀疏性，保证在局部探测时具有常时间计算复杂度。在特征分布密集的环境中，为了减少坐标框架移动的次数，结合现有稀疏化近似方法，可以获得更稀疏的信息矩阵。为了实现大规模环境探测，利用分治法的思想，提出了一种基于约束的子地图方法，根据位置相邻的子地图中公共特征之间的匹配关系、以及子地图使用的局部坐标框架与前一子地图中机器人位姿之间的等价关系，建立加权最小二乘约束。环路闭合后，使用迭代最优化方法求解相应的约束方程，计算这些局部坐标框架的全局位姿，并对每个子地图的末端进行局部调整，获得子地图对应的全局位置，最后得到全局一致的环境地图。最后，针对侦察机器人在探测危险环境时面临的自动回撤问题，本文提出了基于路径点的匹配方法，使用迭代最近点算法逐步调整回撤时的机器人位姿，使当前传感器测量数据与探测阶段记录的关键路径点对应的测量值匹配。对于复杂环境探测，将机器人在所有采样时刻的位姿作为变量，使用原始测量数据直接表示环境，根据加权最小二乘方法，首先得到全局一致的原始数据地图，为自动回撤时定位提供参照。更多还原

【Abstract】 Simultaneous localization and mapping (SLAM) is a problem of joint stateestimation, which builds the environmental map incrementally and estimates itspose when a mobile robot is exploring an unknown environment. This thesismainly uses laser scanner as a range-bearing sensor to measure the relativepositions between the objects in the environment and the robot and to build thepoint feature map. Focusing on the problem of consistency for solving SLAM,some improved methods are put forward and applied to map building ofintelligent vehicle and automatic homing of scout robot.When using an extended Kalman filter (EKF) to solve SLAM, a fullcovariance matrix is utilized to maintain the cross correlations between differentcomponents of the joint state so that the invisible features could be corrected ateach state update step. But the accumulated errors caused by imprecise modelsand linearization make it difficult to keep the covariance matrix accurately,which results in wrong estimate of the state’s mean and produces optimisticestimation. This thesis first validates that the linearization errors will causeinconsistency certainly when the robot keeps stationary and observes newfeatures repeatedly. And the main factor is the big variance of the robot’sheading angle. In order to improve the consistency, a movable coordinate frame(MCF) based SLAM method is proposed, which shifts the reference frame to theplace of the robot’s current pose once a new feature is detected. So the initialvariance of each feature is only determined by the measurement errors and isirrelevant with the uncertainty of the robot pose. Three factors, i.e. the weakinitial correlations between components, the small Kalman gain of the robot’sheading angle and the boundedness of the pose’s uncertainty, provide the basisfor the movement of the coordinate frame. By using the improved method, therobot will explore a known environment all along and the inconsistency causedby linearization errors is limited in local environments. Since it has square computational complexity for EKF to maintain acovariance matrix, an extended information filter (EIF) is used to solve SLAM.This thesis uses a graphical model as the data structure and explains that thelocal performance of EIF could be preserved if the robot’s old pose is reservedwhen moving the coordinate frame and the new pose will has no connectionswith the old features unobserved. In order to have good consistency andcomputational performance concurrently, a MCF based sparse information filteris proposed, which moves the coordinate frame and reserves the robot posewhen the robot switches between local environments. So the information matrixis kept sparse exactly and the exploration in local environments has onlyconstant time complexity. If there are dense features in the environment, anexisting sparsification approximation is utilized to make the information matrixsparser.In order to realize large scale environment exploration, a constraint basedsubmap algorithm is proposed, which utilizes the correspondences between thecommon features in geometrically adjacent submaps and the relationshipsbetween the robot poses and the local refererence frames so that the weightedleast squares constraints between submaps are built. After closing the loop of theenvironment, an equation combining the constraints is calculated by usingiterative optimization. Then the global poses of the local reference frames areyielded, which are followed by the partial adjustment of the tail of each submap.At last, the globally consistent map is acquired.Finally, the problem of automatic homing for a scout robot in the process ofexploring dangerous environments is discussed. A way-point based matchingmethod is proposed, which uses the iterative closest point algorithm to graduallyadjust the returning pose coherent with the recorded way-points. When theenvironment is complicated, the original measurement data without extraction isused to represent the environment. The robot’s pose at each instance is one partof the vector and a big equation with weighted least squares constraints isformed. So a global map with the original representation is acquired, whichcould localize the robot at the returning stage.更多还原