Improved visual SLAM: a novel approach to mapping and localization using visual landmarks in consecutive frames

Pathfinding is becoming more and more common in autonomous vehicle navigation, robot localization, and other computer vision applications. In this paper, a novel approach to mapping and localization is presented that extracts visual landmarks from a robot dataset acquired by a Kinect sensor. The visual landmarks are detected and recognized using the improved scale-invariant feature transform (I-SIFT) method. The methodology is based on detecting stable and invariant landmarks in consecutive (red-green-blue depth) RGB-D frames of the robot dataset. These landmarks are then used to determine the robot path, and a map is constructed by using the visual landmarks. A number of experiments were performed on various datasets in an indoor environment. The proposed method performs efficient landmark detection in various environments, which includes changes in rotation and illumination. The experimental results show that the proposed method can solve the simultaneous localization and mapping (SLAM) problem using stable visual landmarks, but with less computation time.     

基于EKF的全景视觉机器人SLAM算法

研究全景视觉机器人同时定位和地图创建（SLAM）问题。针对普通视觉视野狭窄, 对路标的连续跟踪和定位能力差的问题, 提出了一种基于改进的扩展卡尔曼滤波（EKF）算法的全景视觉机器人SLAM方法, 用全景视觉得到机器人周围的环境信息, 然后从这些信息中提取出环境特征, 定位出路标位置, 进而通过EKF算法同步更新机器人位姿和地图库。仿真实验和实体机器人实验结果验证了该算法的准确性和有效性, 且全景视觉比普通视觉定位精度更高。     

一种改进的FastSLAM算法

同时定位与建图是移动机器人实现真正自治的必要前提,FastSLAM作为一种成功的SLAM方法受到研究者的广泛青睐,FastSLAM将SLAM问题分为一个定位问题和一个建图问题,其中用扩展卡尔曼滤波器（EKF）实现地图陆标的估计与更新,提出了一种改进的FastSLAM方法,用UKF滤波器代替EKF实现FastSLAM中的陆标估计,使得陆标的估计精度提高,该方法同时具有UKF滤波器无需求解观测模型的雅克比矩阵的优点。     

基于视觉的同时定位与地图构建方法综述

基于视觉的自主导航与路径规划是移动机器人研究的关键技术,对基于视觉的计算机导航与同时定位及地图构建（SLAM）方法近三十年的发展进行了总结和展望。将视觉导航分为室内导航和室外导航,并详细阐述了每一种子类型的特点和方法。对于室内视觉导航,列举了经典导航模型和技术方法,探讨了解决SLAM问题的最新进展：HTM-SLAM算法和基于特征的算法;对室外视觉导航,阐述了国际国内目前的研究动态。     

Terrain-Based Navigation for Underwater Vehicles Using an Ultrasonic Scanning System

In this paper an approach to the field of outdoor robotic navigation with a focus on underwater simultaneous localization and mapping (SLAM) is proposed that utilizes ultrasonic scanning images. Experimental results from the implementation of a SLAM algorithm with real data are presented. The projected landmark detection process constructs a map of the environment and generates navigation estimates based on an adaptive delayed nearest-neighbor algorithm. The feature extraction and validation processes are resolved at the sensor level using a simple local maximum-level detection algorithm on the range data. This paper presents experimental results from our research efforts in the above area, using data from water tank trials and a remotely operated vehicle operating in a shallow water environment.     

Mobile-robot pose estimation and environment mapping using an extended Kalman filter

In this paper an extended Kalman filter (EKF) is used in the simultaneous localisation and mapping (SLAM) of a four-wheeled mobile robot in an indoor environment. The robot’s pose and environment map are estimated from incremental encoders and from laser-range-finder (LRF) sensor readings. The map of the environment consists of line segments, which are estimated from the LRF’s scans. A good state convergence of the EKF is obtained using the proposed methods for the input- and output-noise covariance matrices’ estimation. The output-noise covariance matrix, consisting of the observed-line-features’ covariances, is estimated from the LRF’s measurements using the least-squares method. The experimental results from the localisation and SLAM experiments in the indoor environment show the applicability of the proposed approach. The main paper contribution is the improvement of the SLAM algorithm convergence due to the noise covariance matrices’ estimation.     

Convergence and Consistency Analysis for Extended Kalman Filter Based SLAM

This paper investigates the convergence properties and consistency of extended Kalman filter (EKF) based simultaneous localization and mapping (SLAM) algorithms. Proofs of convergence are provided for the nonlinear two-dimensional SLAM problem with point landmarks observed using a range-and-bearing sensor. It is shown that the robot orientation uncertainty at the instant when landmarks are first observed has a significant effect on the limit and/or the lower bound of the uncertainties of the landmark position estimates. This paper also provides some insights to the inconsistencies of EKF based SLAM that have been recently observed. The fundamental cause of EKF SLAM inconsistency for two basic scenarios are clearly stated and associated theoretical proofs are provided.     

Using Embodied Data for Localization and Mapping

Mobile autonomous robots have finally emerged from the confined spaces of structured and controlled indoor environments. To fulfill the promises of ubiquitous robotics in unstructured outdoor environments, robust navigation is a key requirement. The research in the simultaneous localization and mapping (SLAM) community has largely focused on optical sensors to solve this problem, and the fact that the robot is a physical entity has largely been ignored. In this paper, a hierarchical SLAM framework is proposed that takes the interaction of the robot with the environment into account. A sequential Monte Carlo filter is used to generate local map segments with a combination of visual and embodied data associations. Constraints between segments are used to generate globally consistent maps with a focus on suitability for navigation tasks. The proposed method is experimentally verified on two different outdoor robots. The results show that the approach is viable and that the rich modeling of the robot with its environment provides a new modality with the potential for improving existing visual methods and extending the availability of SLAM in domains where visual processing alone is not sufficient.     

Novel Sonar Salient Feature Structure for Extended Kalman Filter-Based Simultaneous Localization and Mapping of Mobile Robots

Abstract

Not all line or point features capable of being extracted by sonar sensors from a cluttered home environment are useful for simultaneous localization and mapping (SLAM) of a mobile robot. This is due to unfavorable conditions such as environmental ambiguity and sonar measurement uncertainty. We present a novel sonar feature structure suitable for a cluttered environment and the extended Kalman filter (EKF)-based SLAM scheme. The key concept is to extract circle feature clouds on salient convex objects by sonar data association called convex saliency circling. The centroid of each circle cloud, called a sonar salient feature, is used as a natural landmark for EKF-based SLAM. By investigating the environmental inherent feature locality, cylindrical objects are augmented conveniently at the weak SLAM-able area as a natural supplementary saliency to achieve consistent SLAM performance. Experimental results demonstrate the validity and robustness of the proposed sonar salient feature structure for EKF-based SLAM.     

Inspection of an underwater structure using point‐cloud SLAM with an AUV and a laser scanner

This paper presents experimental results using a newly developed 3D underwater laser scanner mounted on an autonomous underwater vehicle (AUV) for real‐time simultaneous localization and mapping (SLAM). The algorithm consists of registering point clouds using a dual step procedure. First, a feature‐based coarse alignment is performed, which is then refined using iterative closest point. The robot position is estimated using an extended Kalman filter (EKF) that fuses the data coming from navigation sensors of the AUV. Moreover, the pose from where each point cloud was collected is also stored in the pose‐based EKF‐SLAM state vector. The results of the registration algorithm are used as constraint observations among the different poses within the state vector, solving the full‐SLAM problem. The method is demonstrated using the Girona 500 AUV, equipped with a laser scanner and inspecting a 3D sub‐sea infrastructure inside a water tank. Our results prove that it is possible to limit the navigation drift and deliver a consistent high‐accuracy 3D map of the inspected object.     

A Robust Solution to the Stereo-Vision-Based Simultaneous Localization and Mapping Problem with Steady and Moving Landmarks

The problem of visual simultaneous localization and mapping (SLAM) is examined in this paper using recently developed ideas and algorithms from modern robust control and estimation theory. A nonlinear model for a stereo-vision-based sensor is derived that leads to nonlinear measurements of the landmark coordinates along with optical flow-based measurements of the relative robot–landmark velocity. Using a novel analytical measurement transformation, the nonlinear SLAM problem is converted into the linear domain and solved using a robust linear filter. Actually, the linear filter is guaranteed stable and the SLAM state estimation error is bounded within an ellipsoidal set. A mathematically rigorous stability proof is given that holds true even when the landmarks move in accordance with an unknown control input. No similar results are available for the commonly employed extended Kalman filter, which is known to exhibit divergence and inconsistency characteristics in practice. A number of illustrative examples are given using both simulated and real vision data that further validate the proposed method.     

视觉SLAM的研究现状与展望

针对自主定位与环境构建问题,基于视觉传感器的同时定位与地图构建（SLAM）成为现阶段研究的热点,为深入分析视觉SLAM的现状,综述其相关算法与成果。首先简要概述了视觉SLAM的概念、特点与研究意义;然后深入分析帧间估计算法,详细描述经典的帧间估计方法,其中包含基于特征点的方法、基于光流的方法和直接法,并介绍了经典视觉SLAM算法的标志性成果;之后按照有监督的学习与无监督的学习两种方式介绍深度学习在视觉SLAM中的研究进展,并对算法进行了归纳总结;此外分析了视觉SLAM和惯性导航的融合;最后展望了视觉SLAM的未来发展趋势。     

Bearing-only visual SLAM for small unmanned aerial vehicles in GPS-denied environments

This paper presents a hierarchical simultaneous localization and mapping(SLAM) system for a small unmanned aerial vehicle(UAV) using the output of an inertial measurement unit(IMU) and the bearing-only observations from an onboard monocular camera.A homography based approach is used to calculate the motion of the vehicle in 6 degrees of freedom by image feature match.This visual measurement is fused with the inertial outputs by an indirect extended Kalman filter(EKF) for attitude and velocity estimation.Then,another EKF is employed to estimate the position of the vehicle and the locations of the features in the map.Both simulations and experiments are carried out to test the performance of the proposed system.The result of the comparison with the referential global positioning system/inertial navigation system(GPS/INS) navigation indicates that the proposed SLAM can provide reliable and stable state estimation for small UAVs in GPS-denied environments.     

基于局部子图匹配的SLAM 方法

针对现有的SLAM 解决方法在机器人被“绑架”时失效的问题,提出了基于局部子图匹配的方法．该 方法对现有的SLAM 解决构架进行了改进,提出交点最优匹配的特征相关算法,并且将奇异值分解方法引入机器人 定位．最后,在结构化环境下将本方法和基于扩展卡尔曼滤波器的方法进行比较,讨论了基于局部子图匹配的方法 在结构化环境中解决机器人“绑架”问题的有效性和可行性．     

基于扫描匹配的室外环境SLAM 方法

针对室外环境中的机器人“绑架”问题,提出了基于地图匹配的SLAM方法．该方法舍弃了机器人里程计信息, 只利用局部地图和全局地图的图形相关性进行机器人定位．方法的核心是多重估计数据关联,并将奇异值分解应用到机器人位姿计算中．利用Victoria Park数据集将本算法与基于扩展卡尔曼滤波器的方法进行比较,实验结果证明了本文提出的算法的有效性．     

Autonomous Salient Feature Detection through Salient Cues in an HSV Color Space for Visual Indoor Simultaneous Localization and Mapping

For successful simultaneous localization and mapping (SLAM), perception of the environment is important. This paper proposes a scheme to autonomously detect visual features that can be used as natural landmarks for indoor SLAM. First, features are roughly selected from the camera image through entropy maps that measure the level of randomness of pixel information. Then, the saliency of each pixel is computed by measuring the level of similarity between the selected features and the given image. In the saliency map, it is possible to distinguish the salient features from the background. The robot estimates its pose by using the detected features and builds a grid map of the unknown environment by using a range sensor. The feature positions are stored in the grid map. Experimental results show that the feature detection method proposed in this paper can autonomously detect features in unknown environments reasonably well.     

融合语义激光与地标信息的SLAM技术研究

针对移动机器人在SLAM（即时定位与地图构建）过程中出现的定位失真问题,提出一种通过搭建地标数据库和位姿推导模型,修正机器人错误定位的方法。建图过程中,融合视觉信息与激光数据,得到语义激光,赋予地标语义标签并记录其在地图上的位置信息。导航过程中,当产生定位偏差时,结合多种位姿数据和相对位置关系,推算出机器人在地图上的实际位置,完成重定位。通过实验测试可知,该方法克服了现有机器人在实际室内动态环境下,单一地采用激光或视觉进行定位或重定位技术的缺点和不足,能有效解决“机器人位置漂移问题”。将机器人从当前位置劫持到另一位置,也能根据提出的算法迅速重定位,且定位精度高。     

Visual‐inertial curve simultaneous localization and mapping: Creating a sparse structured world without feature points

We present a simultaneous localization and mapping (SLAM) algorithm that uses Bézier curves as static landmark primitives rather than feature points. Our approach allows us to estimate the full six degrees of freedom pose of a robot while providing a structured map that can be used to assist a robot in motion planning and control. We demonstrate how to reconstruct the three‐dimensional (3D) location of curve landmarks from a stereo pair and how to compare the 3D shape of curve landmarks between chronologically sequential stereo frames to solve the data association problem. We also present a method to combine curve landmarks for mapping purposes, resulting in a map with a continuous set of curves that contain fewer landmark states than conventional point‐based SLAM algorithms. We demonstrate our algorithm's effectiveness with numerous experiments, including comparisons to existing state‐of‐the‐art SLAM algorithms.