Mobile Robot Hierarchical Simultaneous Localization and Mapping Using Monocular Vision

A hierarchical mobile robot simultaneous localization and mapping (SLAM) method that allows us to obtain accurate maps was presented. The local map level is composed of a set of local metric feature maps that are guaranteed to be statistically independent. The global level is a topological graph whose arcs are labeled with the relative location between local maps. An estimation of these relative locations is maintained with local map alignment algorithm, and more accurate estimation is calculated through a global minimization procedure using the loop closure constraint. The local map is built with Rao-Blackwellised particle filter (RBPF), where the particle filter is used to extending the path posterior by sampling new poses. The landmark position estimation and update is implemented through extended Kalman filter (EKF). Monocular vision mounted on the robot tracks the 3D natural point landmarks, which are structured with matching scale invariant feature transform (SIFT) feature pairs. The matching for multi-dimension SIFT features is implemented with a KD-tree in the time cost of O(lbN). Experiment results on Pioneer mobile robot in a real indoor environment show the superior performance of our proposed method.     

纯电动汽车低压电气系统效率研究

为降低纯电动汽车的能耗和延长其续航里程，系统地研究了纯电动汽车低压电气系统效率低下的原因和能量管理策略的改进措施。首先，采用前向仿真方法构建适应长时间快速运行的低压电气系统效率模型，其中，DC/DC变换器效率模型由1阶惯性环节和效率插值函数组成，系统控制模型包括了浮充控制和规则控制两种策略。然后，搭建了系统试验台架，进行部件和系统的性能测试，以提取模型参数。最后，通过仿真和试验研究了负载功率、变换器效率、环境温度、蓄电池类型和控制策略对系统效率的影响。结果表明：系统效率会随着平均功率的减小、变换器效率的降低和蓄电池内阻的增高而下降，怠速时间的缩短、控制策略的改进和蓄电池类型更换都能提升系统效率，且规则控制的锂离子电池低压电气系统在轻载条件下能使系统效率提升10%。