An Efficient Scan-to-Map Matching Approach Based on Multi-channel Lidar

Accurately localizing the vehicle against a pre-built high precision map is an essential step for the Autonomous Land Vehicle (ALV). This paper proposes an efficient scan-to-map matching approach based on multi-channel lidar. We firstly advocate the usage of both the lidar reflectance map and the height map, as these two maps contain complementary information. Then, borrowing ideas from the Lucas-Kanade optical flow approach, we formulate the scan-to-map matching problem in a similar form, and propose an efficient gradient descent approach to solve it. Finally, the proposed approach is integrated into a filtering framework for real-time online localization. Experiments on real-world dataset have demonstrated the validity of our approach.     

A new document representation using term frequency and vectorized graph connectionists with application to document retrieval

This paper presents a new document representation with vectorized multiple features including term frequency and term-connection-frequency. A document is represented by undirected and directed graph, respectively. Then terms and vectorized graph connectionists are extracted from the graphs by employing several feature extraction methods. This hybrid document feature representation more accurately reflects the underlying semantics that are difficult to achieve from the currently used term histograms, and it facilitates the matching of complex graph. In application level, we develop a document retrieval system based on self-organizing map (SOM) to speed up the retrieval process. We perform extensive experimental verification, and the results suggest that the proposed method is computationally efficient and accurate for document retrieval.     

A distance measure between labeled combinatorial maps

Combinatorial maps are widely used in image representation and processing, however map matching problems have not been extensively researched. This paper addresses the problem of inexact matching between labeled combinatorial maps. First, the concept of edit distance is extended to combinatorial maps, and then used to define mapping between combinatorial maps as a sequence of edit operations that transforms one map into another. Subsequently, an optimal approach based on A^* algorithm and an approximate approach based on Greedy algorithm are proposed to compute the distance between combinatorial maps. Experimental results show that the proposed inexact map matching approach produces richer search results than the exact map matching technique by tolerating small difference between maps. The proposed approach performs better in practice than the previous approach based on maximum common submap which cannot be directly used for comparing labels on the maps.     

一种新的基于特征点的立体匹配算法

目前,立体匹配是计算机视觉领域中最活跃的研究主题之一。为了快速并更精确的对特征点进行立体匹配,本文提出了一种新的基于特征点的立体匹配算法。该方法独立于特征点的检测算法,先以扫描线作为匹配单元,然后以鲁棒函数为匹配代价函数,最后用顺序约束对每一匹配单元的视差图进行检测与校正。实验证明,该方法的匹配精度高于传统的基于NCC(norm alized cross-correlation)的立体匹配算法,并且运行时间快,可以应用于纯软件的基于特征点的立体视觉系统中。     

基于雷达–扫描器/惯性导航系统的微小型无人机室内组合导航

本文提出一种基于雷达–扫描器/惯性导航系统(radar-scanner/INS)的微小型无人机室内导航方法.为提高算法的实时性,采用基于扩展卡尔曼滤波(EKF)的DC同步定位与构图技术(SLAM)实现定位和构图;在更新状态值的扫描匹配过程中提出启发性逻辑来筛选激光雷达数据,以提高算法对无人机因姿态和高度变化而引起的轮廓地图波动的抗干扰性;在特征匹配的过程中选取合理的地图轮廓特征,并利用扫描匹配的结果和特征匹配的传递性提出了精度较高的引导配对,以提高特征配对在三维环境下的准确性;最后,将DC SLAM与惯性导航系统进行基于EKF的组合滤波,给出无人机的全状态估计.通过与GPS/INS组合导航对比以及室内飞行验证,本文提出的方法能够满足无人机飞行控制对导航实时性和精度的要求.     

Vision-based global localization for mobile robots with hybrid maps of objects and spatial layouts

This paper presents a novel vision-based global localization that uses hybrid maps of objects and spatial layouts. We model indoor environments with a stereo camera using the following visual cues: local invariant features for object recognition and their 3D positions for object pose estimation. We also use the depth information at the horizontal centerline of image where the optical axis passes through, which is similar to the data from a 2D laser range finder. This allows us to build our topological node that is composed of a horizontal depth map and an object location map. The horizontal depth map describes the explicit spatial layout of each local space and provides metric information to compute the spatial relationships between adjacent spaces, while the object location map contains the pose information of objects found in each local space and the visual features for object recognition. Based on this map representation, we suggest a coarse-to-fine strategy for global localization. The coarse pose is estimated by means of object recognition and SVD-based point cloud fitting, and then is refined by stochastic scan matching. Experimental results show that our approaches can be used for an effective vision-based map representation as well as for global localization methods.     

基于Voronoi地图表示方法的同步定位与地图创建

针对基于混合米制地图机器人同步定位与地图创建 (Simultaneous localization and mapping, SLAM)中地图划分方法不完善的问题, 提出了基于Voronoi地图表示方法的同步定位与地图创建算法VorSLAM. 该算法在全局坐标系下创建特征地图, 并根据此特征地图使用Voronoi图唯一地划分地图空间, 在每一个划分内部创建一个相对于特征的局部稠密地图. 特征地图与各个局部地图最终一起连续稠密地描述了环境. Voronoi地图表示方法解决了地图划分的唯一性问题, 理论证明局部地图可以完整描述该划分所对应的环境轮廓. 该地图表示方法一个基本特点是特征与局部地图一一对应, 每个特征都关联一个定义在该特征上的局部地图. 基于该特点, 提出了一个基于形状匹配的数据关联算法, 用以解决传统数据关联算法出现的多重关联问题. 一个公寓弧形走廊的实验验证了VorSLAM算法和基于形状匹配的数据关联方法的有效性.     

Case-based path planning for autonomous underwater vehicles

Case-based reasoning is reasoning based on specific instances of past experience. A new solution is generated by retrieving and adapting an old one which approximately matches the current situation. In this paper, we outline a case-based reasoning scheme for path planning in autonomous underwater vehicle (AUV) missions. An annotated map database is employed to model the navigational environment. Routes which are used in earlier missions are represented as objects in the map. When a new route is to be planned, the path planner retrieves a matching route from the database and modifies it to suit to the current situation. Whenever a matching route is not available, a new route is synthesized based on past cases that describe similar navigational environments. Case-based approach is thus used not only to adapt old routes but also to synthesize new ones. Since the proposed scheme is centered around reuse of old routes, it would be fast especially when long routes need to be generated. Moreover, better reliability of paths can be expected as they are adapted from earlier missions. The scheme is novel and appropriate for AUV mission scenarios. In this paper, we describe the representation of navigation environment including past routes and objects in the navigational space. Further, we discuss the retrieval and repair strategies and the scheme for synthesizing new routes. Sample results of both synthesis and reuse of routes and system performance analysis are also presented. One major advantage of this system is the facility to enrich the map database with new routes as they are generated.This work was supported in part by National Science Foundation Grant No. BCS-9017990.     

Sparse representation for coarse and fine object recognition

This paper offers a sparse, multiscale representation of objects. It captures the object appearance by selection from a very large dictionary of Gaussian differential basis functions. The learning procedure results from the matching pursuit algorithm, while the recognition is based on polynomial approximation to the bases, turning image matching into a problem of polynomial evaluation. The method is suited for coarse recognition between objects and, by adding more bases, also for fine recognition of the object pose. The advantages over the common representation using PCA include storing sampled points for recognition is not required, adding new objects to an existing data set is trivial because retraining other object models is not needed, and significantly in the important case where one has to scan an image over multiple locations in search for an object, the new representation is readily available as opposed to PCA projection at each location. The experimental result on the COIL-100 data set demonstrates high recognition accuracy with real-time performance.     

A novel illumination-robust face recognition using statistical and non-statistical method

This paper proposes a novel illumination-robust face recognition technique that combines the statistical global illumination transformation and the non-statistical local face representation methods. When a new face image with arbitrary illumination is given, it is transformed into a number of face images exhibiting different illuminations using a statistical bilinear model-based indirect illumination transformation. Each illumination transformed image is then represented by a histogram sequence that concatenates the histograms of the non-statistical multi-resolution uniform local Gabor binary patterns (MULGBP) for all the local regions. This is facilitated by dividing the input image into several regular local regions, converting each local region using several Gabor filters, and converting each Gabor filtered region image into multi-resolution local binary patterns (MULBP). Finally, face recognition is performed by a simple histogram matching process. Experimental results demonstrate that the proposed face recognition method is highly robust to illumination variation as exhibited in the real environment.     

Improved Feature Map Merging Using Virtual Supporting Lines for Multi-Robot Systems

This paper addresses the problem of feature map merging, which is one of the essential techniques for multi-robot systems. If inter-robot measurements are not available for feature map merging, the only way to obtain the map transformation matrix is feature map matching. However, the conventional feature map matching technique requires too much computation time because it has to be iteratively performed to compute the degree of the mismatch between multiple feature maps. This paper proposes a non-iterative feature map merging technique using virtual supporting lines (VSLs) which is also accurate and robust. The proposed technique extracts the spectral information of multiple feature maps using VSLs and obtains the map transformation matrix using the circular cross-correlation between the extracted spectral information of the multiple feature maps. The proposed technique was tested on feature maps produced by experiments with vision sensors, which was performed non-iteratively. In addition, it consistently showed a high acceptance index, which indicates the degree of accuracy for feature map merging.     

Critical Rays Scan Match SLAM

Scan matching is one of the oldest and simplest methods for occupancy grid based SLAM. The general idea is to find the pose of a robot and update its map simply by calculating the 2-D transformation between a laser scan and its predecessor. Due to its simplicity many solutions were proposed and used in various systems, the vast majority of which are iterative. The fact is, that although scan matching is simple in its implementation, it suffers from accumulative noise. Of course, there is certainly a trade-off between the quality of results and the execution time required. Many algorithms have been introduced, in order to achieve good quality maps in a small iteration time, so that on-line execution would be achievable. The proposed SLAM scheme performs scan matching by implementing a ray-selection method. The main idea is to reduce complexity and time needed for matching by pre-processing the scan and selecting rays that are critical for the matching process. In this paper, several different methods of ray-selection are compared. In addition matching is performed between the current scan and the global robot map, in order to minimize the accumulated errors. RRHC (Random Restart Hill Climbing) is employed for matching the scan to the map, which is a local search optimization procedure that can be easily parameterized and is much faster than a traditional genetic algorithm (GA), largely because of the low complexity of the problem. The general idea is to construct a parameterizable SLAM that can be used in an on-line system that requires low computational cost. The proposed algorithm assumes a structured civil environment, is oriented for use in the RoboCup - RoboRescue competition, and its main purpose is to construct high quality maps.     

Identification of space curves from two-dimensional perspective views

This paper describes a new method to be used for matching three-dimensional objects with curved surfaces to two-dimensional perspective views. The method requires for each three-dimensional object a stored model consisting of a closed space curve representing some characteristic connected curved edges of the object. The input is a two-dimensional perspective projection of one of the stored models represented by an ordered sequence of points. The input is converted to a spline representation which is sampled at equal intervals to derive a curvature function. The Fourier transform of the curvature function is used to represent the shape. The actual matching is reduced to a minimization problem which is handled by the Levenberg-Marquardt algorithm [3].     

Metric-topological maps from laser scans adjusted with incremental tree network optimizer

Several adaptations of maximum likelihood approaches to incremental map learning have been proposed recently. In particular, an incremental network optimizer based on stochastic gradient descent provides a fast and easy-to-implement solution to the problem. In this paper, we illustrate two map builders that process laser scans in order to extract the constraint network for the optimization algorithm. The first algorithm builds a map in the form of a collection of scans corresponding to a subset of the poses of a robot moving in the environment. Even though such a solution has the advantage of simplicity, it requires careful processing of data associations. After a preliminary selection of pose constraints candidates, a relative pose is computed through standard scan matching techniques. The second map builder stores a hybrid metric-topological representation: the map consists of a graph whose nodes contain local occupancy grid maps and whose edges are labeled with the relative pose between pairs of nodes. Each patch map summarizes the information of consecutive raw scans and such a richer representation better solves loop closure. Association between pairs of local maps is then performed and tested using correlation-based techniques. Our aim is to illustrate the effectiveness of a tree network optimizer integrated with simple methods for data association. Experiments reported in the paper show that a compact system integrating the optimizer and one of two versions of the map builder works reasonably well with commonly used benchmarks.     

基于霍夫空间模型匹配的移动机器人全局定位方法

提出了一种基于霍夫（Hough）空间模型匹配的全局定位方法．该方法将经典Hough变换引入移动机器人全局定位,利用摄像机获取外界环境的局部地图特征,与给定环境模型（全局地图）在Hough空间进行匹配,由Hough变换可分解性及环境模型相关性分别获取机器人可能的位姿信息,并用一系列高斯值表示,借助求取的位姿方差及其概率分布以及给定环境模型信息剔除不可能位姿,从而最终实现移动机器人全局定位．该方法尤其适用于室内结构化环境. 实验结果表明该方法具有良好的性能．     

Probabilistic voxel mapping using an adaptive confidence measure of stereo matching

In this paper, we describe a probabilistic voxel mapping algorithm using an adaptive confidence measure of stereo matching. Most of the 3D mapping algorithms based on stereo matching usually generate a map formed by point cloud. There are many reconstruction errors. The reconstruction errors are due to stereo reconstruction error factors such as calibration errors, stereo matching errors, and triangulation errors. A point cloud map with reconstruction errors cannot accurately represent structures of environments and needs large memory capacity. To solve these problems, we focused on the confidence of stereo matching and probabilistic representation. For evaluation of stereo matching, we propose an adaptive confidence measure that is suitable for outdoor environments. The confidence of stereo matching can be reflected in the probability of restoring structures. For probabilistic representation, we propose a probabilistic voxel mapping algorithm. The proposed probabilistic voxel map is a more reliable representation of environments than the commonly used voxel map that just contains the occupancy information. We test the proposed confidence measure and probabilistic voxel mapping algorithm in outdoor environments.     

On techniques for optimal noise matching of a class of multimode antenna

This article investigates techniques to design noise‐matching networks for a class of multimodal antennas, specifically, the so‐called quad‐mode antenna. Such an antenna utilizes weighted combinations of four very dissimilar radiation patterns, and different modal input impedances, which vary across scan angle. The matching problem is therefore quite different from that of a classical array, where antenna elements are normally assumed to be similar. In addition to the standard techniques, a new, recursively averaged active impedance, is proposed and applied, as well one using a noise‐active impedance, and two optimization approaches. It is shown for the first time that the quad‐mode antenna displays excellent noise properties, with the simplest technique, namely that of matching to the self‐impedances, producing noise performances across all scan angles which are almost as good as the best solution found by all the techniques.     

Line Segment-Based Indoor Mapping with Salient Line Feature Extraction

We present a method of simultaneous localization and mapping (SLAM) in a large indoor environment using a Rao-Blackwellized particle filter (RBPF) along with a line segment as a landmark. To represent the environment in a compact form, we use only two end points of a line segment, thus reducing computational cost in modeling line segment uncertainty. With a modified scan point clustering method, the proposed adaptive iterative end point fitting contributes to the estimation of line parameters by considering noisy scan points near end points. Thus, by line segment matching the robot is localized well in a local frame. We also introduce an online and offline method of global line merging, which provides a more compact map by removing spurious lines and merging collinear lines. Each of our approaches is efficiently integrated into the proposed RBPF-SLAM framework. In experiments with well-known data sets, the proposed method provides reliable SLAM and compact map representation even in a cluttered environment.