Results 1  10
of
167
Iterative point matching for registration of freeform curves and surfaces
, 1994
"... A heuristic method has been developed for registering two sets of 3D curves obtained by using an edgebased stereo system, or two dense 3D maps obtained by using a correlationbased stereo system. Geometric matching in general is a difficult unsolved problem in computer vision. Fortunately, in ma ..."
Abstract

Cited by 622 (7 self)
 Add to MetaCart
A heuristic method has been developed for registering two sets of 3D curves obtained by using an edgebased stereo system, or two dense 3D maps obtained by using a correlationbased stereo system. Geometric matching in general is a difficult unsolved problem in computer vision. Fortunately, in many practical applications, some a priori knowledge exists which considerably simplifies the problem. In visual navigation, for example, the motion between successive positions is usually approximately known. From this initial estimate, our algorithm computes observer motion with very good precision, which is required for environment modeling (e.g., building a Digital Elevation Map). Objects are represented by a set of 3D points, which are considered as the samples of a surface. No constraint is imposed on the form of the objects. The proposed algorithm is based on iteratively matching points in one set to the closest points in the other. A statistical method based on the distance distribution is used to deal with outliers, occlusion, appearance and disappearance, which allows us to do subsetsubset matching. A leastsquares technique is used to estimate 3D motion from the point correspondences, which reduces the average distance between points in the two sets. Both synthetic and real data have been used to test the algorithm, and the results show that it is efficient and robust, and yields an accurate motion estimate.
Globally Consistent Range Scan Alignment for Environment Mapping
 AUTONOMOUS ROBOTS
, 1997
"... A robot exploring an unknown environmentmay need to build a world model from sensor measurements. In order to integrate all the frames of sensor data, it is essential to align the data properly. An incremental approach has been typically used in the past, in which each local frame of data is alig ..."
Abstract

Cited by 514 (8 self)
 Add to MetaCart
A robot exploring an unknown environmentmay need to build a world model from sensor measurements. In order to integrate all the frames of sensor data, it is essential to align the data properly. An incremental approach has been typically used in the past, in which each local frame of data is aligned to a cumulative global model, and then merged to the model. Because different parts of the model are updated independently while there are errors in the registration, such an approachmay result in an inconsistent model. In this paper, we study the problem of consistent registration of multiple frames of measurements (range scans), together with the related issues of representation and manipulation of spatial uncertainties. Our approachistomaintain all the local frames of data as well as the relative spatial relationships between local frames. These spatial relationships are modeled as random variables and are derived from matching pairwise scans or from odometry. Then we formulat...
A solution to the simultaneous localization and map building (SLAM) problem
 IEEE Transactions on Robotics and Automation
, 2001
"... Abstract—The simultaneous localization and map building (SLAM) problem asks if it is possible for an autonomous vehicle to start in an unknown location in an unknown environment and then to incrementally build a map of this environment while simultaneously using this map to compute absolute vehicle ..."
Abstract

Cited by 467 (29 self)
 Add to MetaCart
(Show Context)
Abstract—The simultaneous localization and map building (SLAM) problem asks if it is possible for an autonomous vehicle to start in an unknown location in an unknown environment and then to incrementally build a map of this environment while simultaneously using this map to compute absolute vehicle location. Starting from the estimationtheoretic foundations of this problem developed in [1]–[3], this paper proves that a solution to the SLAM problem is indeed possible. The underlying structure of the SLAM problem is first elucidated. A proof that the estimated map converges monotonically to a relative map with zero uncertainty is then developed. It is then shown that the absolute accuracy of the map and the vehicle location reach a lower bound defined only by the initial vehicle uncertainty. Together, these results show that it is possible for an autonomous vehicle to start in an unknown location in an unknown environment and, using relative observations only, incrementally build a perfect map of the world and to compute simultaneously a bounded estimate of vehicle location. This paper also describes a substantial implementation of the SLAM algorithm on a vehicle operating in an outdoor environment using millimeterwave (MMW) radar to provide relative map observations. This implementation is used to demonstrate how some key issues such as map management and data association can be handled in a practical environment. The results obtained are crosscompared with absolute locations of the map landmarks obtained by surveying. In conclusion, this paper discusses a number of key issues raised by the solution to the SLAM problem including suboptimal mapbuilding algorithms and map management. Index Terms—Autonomous navigation, millimeter wave radar, simultaneous localization and map building. I.
Recursive estimation of motion, structure, and focal length
 IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
, 1995
"... We present a formulation for recursive recovery of motion, pointwise structure, and focal length from feature correspondences tracked through an image sequence. In addition to adding focal length to the state vector, several representational improvements are made over earlier structure from motion ..."
Abstract

Cited by 291 (11 self)
 Add to MetaCart
We present a formulation for recursive recovery of motion, pointwise structure, and focal length from feature correspondences tracked through an image sequence. In addition to adding focal length to the state vector, several representational improvements are made over earlier structure from motion formulations, yielding a stable and accurate estimation framework which applies uniformly to both true perspective and orthographic projection. Results on synthetic and real imagery illustrate the performance of the estimator.
Kalman Filterbased Algorithms for Estimating Depth from Image Sequences
, 1989
"... Using known camera motion to estimate depth from image sequences is an important problem in robot vision. Many applications of depthfrommotion, including navigation and manipulation, require algorithms that can estimate depth in an online, incremental fashion. This requires a representation that ..."
Abstract

Cited by 249 (25 self)
 Add to MetaCart
Using known camera motion to estimate depth from image sequences is an important problem in robot vision. Many applications of depthfrommotion, including navigation and manipulation, require algorithms that can estimate depth in an online, incremental fashion. This requires a representation that records the uncertainty in depth estimates and a mechanism that integrates new measurements with existing depth estimates to reduce the uncertainty over time. Kalman filtering provides this mechanism. Previous applications of Kalman filtering to depthfrommotion have been limited to estimating depth at the location of a sparse set of features. In this paper, we introduce a new, pixelbased (iconic) algorithm that estimates depth and depth uncertainty at each pixel and incrementally refines these estimates over time. We describe the algorithm and contrast its formulation and performance to that of a featurebased Kalman filtering algorithm. We compare the performance of the two approaches by analyzing their theoretical convergence rates, by conducting quantitative experiments with images of a flat poster, and by conducting qualitative experiments with images of a realistic outdoorscene model. The results show that the new method is an effective way to extract depth from lateral camera translations. This approach can be extended to incorporate general motion and to integrate other sources of information, such as stereo. The algorithms we have developed, which combine Kalman filtering with iconic descriptions of depth, therefore can serve as a useful and general framework for lowlevel dynamic vision.
Vision for Mobile Robot Navigation: A Survey
 IEEE, TRANS. PAMI
, 2002
"... This paper surveys the developments of the last 20 years in the area of vision for mobile robot navigation. Two major components of the paper deal with indoor navigation and outdoor navigation. For each component, we have further subdivided our treatment of the subject on the basis of structured an ..."
Abstract

Cited by 212 (2 self)
 Add to MetaCart
This paper surveys the developments of the last 20 years in the area of vision for mobile robot navigation. Two major components of the paper deal with indoor navigation and outdoor navigation. For each component, we have further subdivided our treatment of the subject on the basis of structured and unstructured environments. For indoor robots in structured environments, we have dealt separately with the cases of geometrical and topological models of space. For unstructured environments, we have discussed the cases of navigation using optical flows, using methods from the appearancebased paradigm, and by recognition of specific objects in the environment.
Robust mapping and localization in indoor environments using sonar data
 Int. J. Robotics Research
, 2002
"... In this paper we describe a new technique for the creation of featurebased stochastic maps using standard Polaroid sonar sensors. The fundamental contributions of our proposal are: (1) a perceptual grouping process that permits the robust identification and localization of environmental features, su ..."
Abstract

Cited by 171 (30 self)
 Add to MetaCart
(Show Context)
In this paper we describe a new technique for the creation of featurebased stochastic maps using standard Polaroid sonar sensors. The fundamental contributions of our proposal are: (1) a perceptual grouping process that permits the robust identification and localization of environmental features, such as straight segments and corners, from the sparse and noisy sonar data; (2) a map joining technique that allows the system to build a sequence of independent limitedsize stochastic maps and join them in a globally consistent way; (3) a robust mechanism to determine which features in a stochastic map correspond to the same environment feature, allowing the system to update the stochastic map accordingly, and perform tasks such as revisiting and loop closing. We demonstrate the practicality of this approach by building a geometric map of a medium size, real indoor environment, with several people moving around the robot. Maps built from laser data for the same experiment are provided for comparison. Key words
A Review of Statistical Data Association Techniques for Motion Correspondence
 International Journal of Computer Vision
, 1993
"... Motion correspondence is a fundamental problem in computer vision and many other disciplines. This article describes statistical data association techniques originally developed in the context of target tracking and surveillance and now beginning to be used in dynamic motion analysis by the computer ..."
Abstract

Cited by 135 (3 self)
 Add to MetaCart
(Show Context)
Motion correspondence is a fundamental problem in computer vision and many other disciplines. This article describes statistical data association techniques originally developed in the context of target tracking and surveillance and now beginning to be used in dynamic motion analysis by the computer vision community. The Mahalanobis distance measure is first introduced before discussing the limitations of nearest neighbor algorithms. Then, the tracksplitting, joint likelihood, multiple hypothesis algorithms are described, each method solving an increasingly more complicated optimization. Realtime constraints may prohibit the application of these optimal methods. The suboptimal joint probabilistic data association algorithm is therefore described. The advantages, limitations, and relationships between the approaches are discussed. 1
Square Root SAM: Simultaneous localization and mapping via square root information smoothing
 International Journal of Robotics Reasearch
, 2006
"... Solving the SLAM problem is one way to enable a robot to explore, map, and navigate in a previously unknown environment. We investigate smoothing approaches as a viable alternative to extended Kalman filterbased solutions to the problem. In particular, we look at approaches that factorize either th ..."
Abstract

Cited by 135 (39 self)
 Add to MetaCart
(Show Context)
Solving the SLAM problem is one way to enable a robot to explore, map, and navigate in a previously unknown environment. We investigate smoothing approaches as a viable alternative to extended Kalman filterbased solutions to the problem. In particular, we look at approaches that factorize either the associated information matrix or the measurement Jacobian into square root form. Such techniques have several significant advantages over the EKF: they are faster yet exact, they can be used in either batch or incremental mode, are better equipped to deal with nonlinear process and measurement models, and yield the entire robot trajectory, at lower cost for a large class of SLAM problems. In addition, in an indirect but dramatic way, column ordering heuristics automatically exploit the locality inherent in the geographic nature of the SLAM problem. In this paper we present the theory underlying these methods, along with an interpretation of factorization in terms of the graphical model associated with the SLAM problem. We present both simulation results and actual SLAM experiments in largescale environments that underscore the potential of these methods as an alternative to EKFbased approaches. 1
Visionbased global localization and mapping for mobile robots
 IEEE Transactions on Robotics
, 2005
"... Abstract—We have previously developed a mobile robot system which uses scaleinvariant visual landmarks to localize and simultaneously build threedimensional (3D) maps of unmodified environments. In this paper, we examine global localization, where the robot localizes itself globally, without any ..."
Abstract

Cited by 109 (3 self)
 Add to MetaCart
(Show Context)
Abstract—We have previously developed a mobile robot system which uses scaleinvariant visual landmarks to localize and simultaneously build threedimensional (3D) maps of unmodified environments. In this paper, we examine global localization, where the robot localizes itself globally, without any prior location estimate. This is achieved by matching distinctive visual landmarks in the current frame to a database map. A Hough transform approach and a RANSAC approach for global localization are compared, showing that RANSAC is much more efficient for matching specific features, but much worse for matching nonspecific features. Moreover, robust global localization can be achieved by matching a small submap of the local region built from multiple frames. This submap alignment algorithm for global localization can be applied to map building, which can be regarded as alignment of multiple 3D submaps. A global minimization procedure is carried out using the loop closure constraint to avoid the effects of slippage and drift accumulation. Landmark uncertainty is taken into account in the submap alignment and the global minimization process. Experiments show that global localization can be achieved accurately using the scaleinvariant landmarks. Our approach of pairwise submap alignment with backward correction in a consistent manner produces a better global 3D map. Index Terms—Global localization, map building, mobile robots, visual landmarks. I.