基于分级关键帧筛选的RGB-DSLAM算法研究

利用关键帧求解SLAM算法(simultaneous localization and mapping)能够提高SLAM系统的实时性与精确度。针对现存关键帧筛选算法中存在的计算复杂度高、图像帧冗余以及鲁棒性较差等问题,提出一种分级关键帧筛选方法。该算法考虑了SLAM系统在不同运行阶段时对关键帧的要求,首先结合旋转度指数与地图点跟踪筛选出一级关键帧用于后端优化与回环检测,再利用相邻帧在空间上的相对运动距离筛选出二级关键帧用于三维地图构建,最后,实现了基于此二级筛选算法的RGB-D SLAM系统。实验表明,一级关键帧算法能提高SLAM系统的定位和建图精度,二级关键帧算法则有效减少了数据冗余,提高了建图效率。     

SLAM无损检测技术的研究

本文分析了声波对有损耗样品板的透射规律，得到了透射率|W|^2的公式。对于两类样品板，通过数值计算得到了它们的响应曲线|W|^2θ，通过调节入射角θ1得到|W|^2的最大值。所做工作有利于提高SLAM的检测灵敏度。     

基于即时稠密三维重构的无人机视觉定位

传统景象匹配定位方法在用于低空无人机定位时,易因低空航拍图像视场小,且与卫星图像（带有地理信息）的拍摄角度差异大而失败.本文提出了一种基于即时稠密三维重构的无人机视觉定位方法,通过将稠密三维点云与卫星图像匹配以实现无人机定位.首先根据图像序列快速估计摄像机位姿,而后使用多深度图协同去噪与优化算法生成稠密三维点云,随后通过变换观察视角由稠密三维点云生成与卫星图像拍摄视角相近的虚拟视图,最后将虚拟视图与卫星图像匹配并得到无人机的地理坐标.由于稠密三维点云包含多张图像的信息,覆盖面积大,且可变化观察视角,因此能够有效克服上述两个问题.实验证明了本文方法的有效性.     

A Modified Particle Filter for Simultaneous Localization and Mapping

The implementation of a particle filter (PF) for vision-based bearing-only simultaneous localization and mapping (SLAM) of a mobile robot in an unstructured indoor environment is presented in this paper. Variations, using techniques from the genetic algorithm (GA), to standard PF procedures are proposed to alleviate the sample impoverishment problem. A monochrome CCD camera mounted on the robot is used as the measuring device and a measure on the image quality is incorporated into data association and PF update. Since the bearing-only measurement does not contain range information, we add a pseudo range to the measurement during landmark initialization as a hypothesised pair and the non-promising landmark is removed by a map management strategy. Simulation and experimental results from an implementation using real-life data acquired from a Pioneer robot are included to demonstrate the effectiveness of our approach.     

抛光型SLAM系统灵敏度研究

以片式电子材料元器件作为无损检测分析对象,以提高激光扫描声学显微镜(SLAM)系统灵敏度为目标,详细分析了入射声波在抛光型分层介质中的传播规律,建立了抛光型样品自由界面动态波纹振幅数学模型。经综合分析获得了提高系统响应的方法,为进一步改进SLAM提供了理论依据。     

Vision-Based SLAM: Stereo and Monocular Approaches

Building a spatially consistent model is a key functionality to endow a mobile robot with autonomy. Without an initial map or an absolute localization means, it requires to concurrently solve the localization and mapping problems. For this purpose, vision is a powerful sensor, because it provides data from which stable features can be extracted and matched as the robot moves. But it does not directly provide 3D information, which is a difficulty for estimating the geometry of the environment. This article presents two approaches to the SLAM problem using vision: one with stereovision, and one with monocular images. Both approaches rely on a robust interest point matching algorithm that works in very diverse environments. The stereovision based approach is a classic SLAM implementation, whereas the monocular approach introduces a new way to initialize landmarks. Both approaches are analyzed and compared with extensive experimental results, with a rover and a blimp.     

An autonomous mobile robot with a 3D laser range finder for 3D exploration and digitalization of indoor environments

Digital 3D models of the environment are needed in rescue and inspection robotics, facility managements and architecture. This paper presents an automatic system for gaging and digitalization of 3D indoor environments. It consists of an autonomous mobile robot, a reliable 3D laser range finder and three elaborated software modules. The first module, a fast variant of the Iterative Closest Points algorithm, registers the 3D scans in a common coordinate system and relocalizes the robot. The second module, a next best view planner, computes the next nominal pose based on the acquired 3D data while avoiding complicated obstacles. The third module, a closed-loop and globally stable motor controller, navigates the mobile robot to a nominal pose on the base of odometry and avoids collisions with dynamical obstacles. The 3D laser range finder acquires a 3D scan at this pose. The proposed method allows one to digitalize large indoor environments fast and reliably without any intervention and solves the SLAM problem. The results of two 3D digitalization experiments are presented using a fast octree-based visualization method.     

Using Absolute Metric Maps to Close Cycles in a Topological Map

In simultaneous localisation and mapping (SLAM) the correspondence problem, specifically detecting cycles, is one of the most difficult challenges for an autonomous mobile robot. In this paper we show how significant cycles in a topological map can be identified with a companion absolute global metric map. A tight coupling of the basic unit of representation in the two maps is the key to the method. Each local space visited is represented, with its own frame of reference, as a node in the topological map. In the global absolute metric map these local space representations from the topological map are described within a single global frame of reference. The method exploits the overlap which occurs when duplicate representations are computed from different vantage points for the same local space. The representations need not be exactly aligned and can thus tolerate a limited amount of accumulated error. We show how false positive overlaps which are the result of a misaligned map, can be discounted.     

Large scale multiple robot visual mapping with heterogeneous landmarks in semi-structured terrain

This paper addresses the cooperative localization and visual mapping problem with multiple heterogeneous robots. The approach is designed to deal with the challenging large semi-structured outdoors environments in which aerial/ground ensembles are to evolve. We propose the use of heterogeneous visual landmarks, points and line segments, to achieve effective cooperation in such environments. A large-scale SLAM algorithm is generalized to handle multiple robots, in which a global graph maintains the relative relationships between a series of local sub-maps built by the different robots. The key issue when dealing with multiple robots is to find the link between them, and to integrate these relations to maintain the overall geometric consistency; the events that introduce these links on the global graph are described in detail. Monocular cameras are considered as the primary extereoceptive sensor. In order to achieve the undelayed initialization required by the bearing-only observations, the well-known inverse-depth parametrization is adopted to estimate 3D points. Similarly, to estimate 3D line segments, we present a novel parametrization based on anchored Plücker coordinates, to which extensible endpoints are added. Extensive simulations show the proposed developments, and the overall approach is illustrated using real-data taken with a helicopter and a ground rover.