基于粒子滤波的移动机器人SLAM改进算法

针对未知环境中移动机器人同时定位和地图创建（Simultaneous Localization and Map Building,SLAM）由于机器人位姿和环境地图都不确定导致定位和地图创建变得更加复杂,提出一种局部最优（全局次优）参数法,即通过局部最优的位姿创建局部最优的环境地图,再通过局部最优的环境地图寻求局部最优的位姿,如此交替进行,直到得到全局确定性的位姿和确定性的环境地图。实验结果表明,同标准的基于粒子滤波的SLAM 算法（Particle Filtering-SLAM,PF-SLAM）比较,改进的算法提高了机器人SLAM过程中定位的准确度和地图创建的精确度,为机器人在未知的室外大环境同时定位和地图创建提供新的方法。     

Real-time hierarchical stereo Visual SLAM in large-scale environments

In this paper we present a new real-time hierarchical (topological/metric) Visual SLAM system focusing on the localization of a vehicle in large-scale outdoor urban environments. It is exclusively based on the visual information provided by a cheap wide-angle stereo camera. Our approach divides the whole map into local sub-maps identified by the so-called fingerprints (vehicle poses). At the sub-map level (low level SLAM), 3D sequential mapping of natural landmarks and the robot location/orientation are obtained using a top-down Bayesian method to model the dynamic behavior. A higher topological level (high level SLAM) based on fingerprints has been added to reduce the global accumulated drift, keeping real-time constraints. Using this hierarchical strategy, we keep the local consistency of the metric sub-maps, by mean of the EKF, and global consistency by using the topological map and the MultiLevel Relaxation (MLR) algorithm. Some experimental results for different large-scale outdoor environments are presented, showing an almost constant processing time.     

Dual FastSLAM: Dual Factorization of the Particle Filter Based Solution of the Simultaneous Localization and Mapping Problem

The process of building a map with a mobile robot is known as the Simultaneous Localization and Mapping (SLAM) problem, and is considered essential for achieving true autonomy. The best existing solutions to the SLAM problem are based on probabilistic techniques, mainly derived from the basic Bayes Filter. A recent approach is the use of Rao-Blackwellized particle filters. The FastSLAM solution factorizes the Bayes SLAM posterior using a particle filter to estimate over the possible paths of the robot and several independent Kalman Filters attached to each particle to estimate the location of landmarks conditioned to the robot path. Although there are several successful implementations of this idea, there is a lack of applications to indoor environments where the most common feature is the line segment corresponding to straight walls. This paper presents a novel factorization, which is the dual of the existing FastSLAM one, that decouples the SLAM into a map estimation and a localization problem, using a particle filter to estimate over maps and a Kalman Filter attached to each particle to estimate the robot pose conditioned to the given map. We have implemented and tested this approach, analyzing and comparing our solution with the FastSLAM one, and successfully building feature based maps of indoor environments.     

Opportunistic sampling-based active visual SLAM for underwater inspection

This paper reports on an active SLAM framework for performing large-scale inspections with an underwater robot. We propose a path planning algorithm integrated with visual SLAM that plans loop-closure paths in order to decrease navigation uncertainty. While loop-closing revisit actions bound the robot’s uncertainty, they also lead to redundant area coverage and increased path length. Our proposed opportunistic framework leverages sampling-based techniques and information filtering to plan revisit paths that are coverage efficient. We employ Gaussian process regression for modeling the prediction of camera registrations and use a two-step optimization procedure for selecting revisit actions. We show that the proposed method offers many benefits over existing solutions and good performance for bounding navigation uncertainty in long-term autonomous operations with hybrid simulation experiments and real-world field trials performed by an underwater inspection robot.     

基于主动环形闭合约束的移动机器人分层同时定位和地图创建

基于Rao-Blackwellized粒子滤波器提出了一种基于主动闭环策略的移动机器人分层同时定位和地图创建(simultaneous localization and mapping,SLAM)方法,基于信息熵的主动闭环策略同时考虑机器人位姿和地图的不确定性;局部几何特征地图之间的相对关系通过一致性算法估计,并通过环形闭合约束的最小化过程回溯修正.在仅有单目视觉和里程计的基础上,建立了鲁棒的感知模型;通过有效的尺度不变特征变换(scale invariant feature transform,SIFT)方法提取环境特征,基于KD-Tree的最近邻搜索算法实现特征匹配.实际实验表明该方法为实现SLAM提供了一种有效可靠的途径.     

Hybrid robot control and SLAM for persistent navigation and mapping

For a mobile robot to operate autonomously in real-world environments, it must have an effective control system and a navigation system capable of providing robust localization, path planning and path execution. In this paper we describe work investigating synergies between mapping and control systems. We have integrated development of a control system for navigating mobile robots and a robot SLAM system. The control system is hybrid in nature and tightly coupled with the SLAM system; it uses a combination of high and low level deliberative and reactive control processes to perform obstacle avoidance, exploration, global navigation and recharging, and draws upon the map learning and localization capabilities of the SLAM system. The effectiveness of this hybrid, multi-level approach was evaluated in the context of a delivery robot scenario. Over a period of two weeks the robot performed 1143 delivery tasks to 11 different locations with only one delivery failure (from which it recovered), travelled a total distance of more than 40 km, and recharged autonomously a total of 23 times. In this paper we describe the combined control and SLAM system and discuss insights gained from its successful application in a real-world context.     

Robust Navigation and Mapping Architecture for Large Environments

This paper addresses the problem of Simultaneous Localization and Mapping (SLAM) for very large environments. A hybrid architecture is presented that makes use of the Extended Kalman Filter to perform SLAM in a very efficient form and a Monte Carlo localizer to resolve data association problems potentially present when returning to a known location after a large exploration period. Algorithms to improve the convergence of the Monte Carlo filter are presented that consider vehicle and sensor uncertainty. The proposed algorithm incorporates significant integrity to the standard SLAM algorithms by providing the ability to handle multimodal distributions over robot pose in real time during the re‐localization process. Experimental results in outdoor environments are presented to demonstrate the performance of the algorithm proposed. © 2003 Wiley Periodicals, Inc.     

A Hybrid Bayesian-Frequentist Approach to SLAM

A hybrid Bayesian/ frequentist approach is presented for the Simultaneous Localization and Mapping Problem (SLAM). A frequentist approach is proposed for mapping a dense environment when the robotic pose is known and then extended to the case when the pose is uncertain. The SLAM problem is then solved in two steps: 1) the robot is localized with respect to a sparse set of landmarks in the map using a Bayes filter and a belief on the robot pose is formed, and 2) this belief on the robot pose is used to map the rest of the map using the frequentist estimator. The frequentist part of the hybrid methodology is shown to have complexity linear (constant time complexity under the assumption of bounded noise) in the map components, is robust to the data association problem and is provably consistent. The complexity of the Bayesian part is kept under control owing to the sparseness of the features, which also improves the robustness of the technique to the issue of data association. The hybrid method is tested on standard datasets on the RADISH repository.     

Large scale graph-based SLAM using aerial images as prior information

The problem of learning a map with a mobile robot has been intensively studied in the past and is usually referred to as the simultaneous localization and mapping (SLAM) problem. However, most existing solutions to the SLAM problem learn the maps from scratch and have no means for incorporating prior information. In this paper, we present a novel SLAM approach that achieves global consistency by utilizing publicly accessible aerial photographs as prior information. It inserts correspondences found between stereo and three-dimensional range data and the aerial images as constraints into a graph-based formulation of the SLAM problem. We evaluate our algorithm based on large real-world datasets acquired even in mixed in- and outdoor environments by comparing the global accuracy with state-of-the-art SLAM approaches and GPS. The experimental results demonstrate that the maps acquired with our method show increased global consistency.     

一种改进的粒子滤波SLAM算法*

提出一种改进的粒子滤波SLAM（simultaneous localization and map building）同时定位和地图创建实现方法。改进方法让机器人大约行进10步完成基于局部已创建地图下的粒子滤波定位后,再利用激光传感器探测环境并更新创建的地图;同时在利用粒子滤波定位时,使粒子只分布在由航位推算法得出的机器人位姿附近,从而可有效地减少粒子的数量。实验结果表明,与标准的粒子滤波SLAM 算法比较,改进算法提高了机器人SLAM过程中定位和地图创建的精度和实时性,并为移动机器人在室外未知环境同时定位和地图创建提供了新方法。     

A multilevel relaxation algorithm for simultaneous localization and mapping

This paper addresses the problem of simultaneous localization and mapping (SLAM) by a mobile robot. An incremental SLAM algorithm is introduced that is derived from multigrid methods used for solving partial differential equations. The approach improves on the performance of previous relaxation methods for robot mapping, because it optimizes the map at multiple levels of resolution. The resulting algorithm has an update time that is linear in the number of estimated features for typical indoor environments, even when closing very large loops, and offers advantages in handling nonlinearities compared with other SLAM algorithms. Experimental comparisons with alternative algorithms using two well-known data sets and mapping results on a real robot are also presented.     

Fast, On-Line Learning of Globally Consistent Maps

To navigate in unknown environments, mobile robots require the ability to build their own maps. A major problem for robot map building is that odometry-based dead reckoning cannot be used to assign accurate global position information to a map because of cumulative drift errors. This paper introduces a fast, on-line algorithm for learning geometrically consistent maps using only local metric information. The algorithm works by using a relaxation technique to minimize an energy function over many small steps. The approach differs from previous work in that it is computationally cheap, easy to implement and is proven to converge to a globally optimal solution. Experiments are presented in which large, complex environments were successfully mapped by a real robot.     

A frequentist approach to mapping under uncertainty

An asynchronous stochastic approximation based (frequentist) approach is proposed for mapping using noisy mobile sensors under two different scenarios: (1) perfectly known sensor locations and (2) uncertain sensor locations. The frequentist methodology has linear complexity in the map components, is immune to the data association problem and is provably consistent. The frequentist methodology, in conjunction with a Bayesian estimator, is applied to the Simultaneous Localization and Mapping (SLAM) problem of Robotics. Several large maps are estimated using the hybrid Bayesian/Frequentist scheme and results show that the technique is robust to the computational and performance issues inherent in the purely Bayesian approaches to the problem.     

Supervised semantic labeling of places using information extracted from sensor data

Indoor environments can typically be divided into places with different functionalities like corridors, rooms or doorways. The ability to learn such semantic categories from sensor data enables a mobile robot to extend the representation of the environment facilitating interaction with humans. As an example, natural language terms like “corridor” or “room” can be used to communicate the position of the robot in a map in a more intuitive way. In this work, we first propose an approach based on supervised learning to classify the pose of a mobile robot into semantic classes. Our method uses AdaBoost to boost simple features extracted from sensor range data into a strong classifier. We present two main applications of this approach. Firstly, we show how our approach can be utilized by a moving robot for an online classification of the poses traversed along its path using a hidden Markov model. In this case we additionally use as features objects extracted from images. Secondly, we introduce an approach to learn topological maps from geometric maps by applying our semantic classification procedure in combination with a probabilistic relaxation method. Alternatively, we apply associative Markov networks to classify geometric maps and compare the results with a relaxation approach. Experimental results obtained in simulation and with real robots demonstrate the effectiveness of our approach in various indoor environments.     

基于分布式感知的移动机器人同时定位与地图创建

为了创建大规模环境的精确栅格地图,提出一种基于分布式感知的两层同时定位与地图创建（SLAM）算法．在局部层,机器人一旦进入了一个新的摄像头视野,便依据机器人本体上的激光和里程计信息,采用Rao-Blackwellized粒子滤波方法创建一个新的局部栅格地图．与此同时,带有检测标志的机器人在摄像头视野内以曲线方式运动,以解决该摄像头的标定问题．在全局层,一系列的局部地图组成一个连接图,局部地图间的约束对应于连接图的边．为了生成一个准确且全局一致的环境地图,采用随机梯度下降法对连接图进行优化．实验结果验证了所提算法的有效性．     

Learned Action SLAM: Sharing SLAM through learned path planning information between heterogeneous robotic platforms

Currently when path planning is used in SLAM it is to benefit SLAM only, with no mutual benefit for path planning. Furthermore, SLAM algorithms are generally implemented and modified for individual heterogeneous robotic platforms without autonomous means of sharing navigation information. This limits the ability for robot platforms to share navigation information and can require heterogeneous robot platforms to generate individual maps within the same environment. This paper introduces Learned Action SLAM, which for the first time autonomously combines path-planning with SLAM such that heterogeneous robots can share learnt knowledge through Learning Classifier Systems (LCS). This is in contrast to Active SLAM, where path-planning is used to benefit SLAM only. Results from testing LA-SLAM on robots in the real world have shown; promise for use on teams of robots with various sensor morphologies, implications for scaling to associated domains, and ability to share maps taken from less capable to more advanced robots.     

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。     

Active SLAM and Exploration with Particle Filters Using Kullback-Leibler Divergence

Autonomous exploration under uncertain robot location requires the robot to use active strategies to trade-off between the contrasting tasks of exploring the unknown scenario and satisfying given constraints on the admissible uncertainty in map estimation. The corresponding problem, namely active SLAM (Simultaneous Localization and Mapping) and exploration, has received a large attention from the robotic community for its relevance in mobile robotics applications. In this work we tackle the problem of active SLAM and exploration with Rao-Blackwellized Particle Filters. We propose an application of Kullback-Leibler divergence for the purpose of evaluating the particle-based SLAM posterior approximation. This metric is then applied in the definition of the expected information from a policy, which allows the robot to autonomously decide between exploration and place revisiting actions (i.e., loop closing). Extensive tests are performed in typical indoor and office environments and on well-known benchmarking scenarios belonging to SLAM literature, with the purpose of comparing the proposed approach with the state-of-the-art techniques and to evaluate the maturity of truly autonomous navigation systems based on particle filtering.     

A constrained SLAM approach to robust and accurate localisation of autonomous ground vehicles

For driving assistance systems, intelligent vehicles and autonomous robots to be viable in complex environments, it is necessary to have a reliable and robust localisation function. Due to the large variability and uncertainty of such complex environments, which include theme parks, university campuses, suburbs, industrial estates and the like, it is difficult to rely on a specific method or set of sensor data to correctly and robustly estimate the robot path/pose. The key to solving the localisation problem is to optimally use and fuse all useful sources of information available to the mobile platform. For the envisaged environment, it is not unusual to have approximate digital maps of the road network. In this paper, in addition to the typical sensory information provided by extereoceptive and proprioceptive sensors, it is shown how a priori approximate knowledge available in the form of a road map can be systematically fused within a Simultaneous Localisation and Map Building (SLAM) framework to obtain more accurate and robust localisation results. This reformulation of SLAM through the introduction of constraints in the form of a priori map information not only makes the problem theoretically more correct in the sense of observability but also makes the system viable and effective, yielding more accurate results. The results obtained in an actual environment are presented to validate the claims.