Simultaneous localization and mapping with sparse extended information filters (2004)

by S Thrun, Y Liu, D Koller, A Y Ng, Z Ghahramani, H DurrantWhyte
Venue:Int. J. Robot. Res
Add To MetaCart
Results 1 - 10 of 254

Improved techniques for grid mapping with rao-blackwellized particle filters

by Giorgio Grisetti, Cyrill Stachniss, Wolfram Burgard - IEEE Transactions on Robotics , 2007
"... Abstract — Recently, Rao-Blackwellized particle filters have been introduced as an effective means to solve the simultaneous localization and mapping problem. This approach uses a particle filter in which each particle carries an individual map of the environment. Accordingly, a key question is how ..."
Abstract - Cited by 179 (20 self) - Add to MetaCart
Abstract — Recently, Rao-Blackwellized particle filters have been introduced as an effective means to solve the simultaneous localization and mapping problem. This approach uses a particle filter in which each particle carries an individual map of the environment. Accordingly, a key question is how to reduce the number of particles. In this paper, we present adaptive techniques for reducing this number in a Rao-Blackwellized particle filter for learning grid maps. We propose an approach to compute an accurate proposal distribution taking into account not only the movement of the robot but also the most recent observation. This drastically decreases the uncertainty about the robot’s pose in the prediction step of the filter. Furthermore, we present an approach to selectively carry out resampling operations which seriously reduces the problem of particle depletion. Experimental results carried out with real mobile robots in large-scale indoor as well as in outdoor environments illustrate the advantages of our methods over previous approaches. Index Terms — SLAM, Rao-Blackwellized particle filter, adaptive resampling, motion-model, improved proposal
(Show Context)

Square Root SAM: Simultaneous localization and mapping via square root information smoothing

by Frank Dellaert, Michael Kaess , 2006
"... Solving the SLAM (simultaneous localization and mapping) problem is one way to enable a robot to explore, map, and navigate in a previously unknown environment. Smoothing approaches have been investigated as a viable alternative to extended Kalman filter (EKF)based solutions to the problem. In parti ..."
Abstract - Cited by 144 (38 self) - Add to MetaCart
Solving the SLAM (simultaneous localization and mapping) problem is one way to enable a robot to explore, map, and navigate in a previously unknown environment. Smoothing approaches have been investigated as a viable alternative to extended Kalman filter (EKF)based solutions to the problem. In particular, approaches have been looked at 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 non-linear 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. This paper presents the theory underlying these methods, along with an interpretation of factorization in terms of the graphical model associated with the SLAM problem. Both simulation results and actual SLAM experiments in large-scale environments are presented that underscore the potential of these methods as an alternative to EKF-based approaches.
(Show Context)

Thin Junction Tree Filters for Simultaneous Localization and Mapping

by Mark A. Paskin, Mark A. Paskin - In Intl. Joint Conf. on Artificial Intelligence (IJCAI , 2003
"... Simultaneous Localization and Mapping (SLAM) is a fundamental problem in mobile robotics: while a robot navigates in an unknown environment, it must incrementally build a map of its surroundings and localize itself within that map. Traditional approaches to the problem are based upon Kalman filters, ..."
Abstract - Cited by 128 (1 self) - Add to MetaCart
Simultaneous Localization and Mapping (SLAM) is a fundamental problem in mobile robotics: while a robot navigates in an unknown environment, it must incrementally build a map of its surroundings and localize itself within that map. Traditional approaches to the problem are based upon Kalman filters, but suffer from complexity issues: the size of the belief state and the time complexity of the filtering operation grow quadratically in the size of the map. This paper presents a filtering technique that maintains a tractable approximation of the filtered belief state as a thin junction tree. The junction tree grows under measurement and motion updates and is periodically "thinned" to remain tractable via efficient maximum likelihood projections. When applied to the SLAM problem, these thin junction tree filters have a linear-space belief state representation, and use a linear-time filtering operation. Further approximation can yield a constant-time filtering operation, at the expense of delaying the incorporation of observations into the majority of the map. Experiments on a suite of SLAM problems validate the approach.

Scalable monocular SLAM

by Ethan Eade, Tom Drummond - in IEEE Computer Society Conference on Computer Vision and Pattern Recognition , 2006
"... Localization and mapping in unknown environments becomes more difficult as the complexity of the environment increases. With conventional techniques, the cost of maintaining estimates rises rapidly with the number of landmarks mapped. We present a monocular SLAM system that employs a particle filter ..."
Abstract - Cited by 117 (3 self) - Add to MetaCart
Localization and mapping in unknown environments becomes more difficult as the complexity of the environment increases. With conventional techniques, the cost of maintaining estimates rises rapidly with the number of landmarks mapped. We present a monocular SLAM system that employs a particle filter and top-down search to allow realtime performance while mapping large numbers of landmarks. To our knowledge, we are the first to apply this FastSLAM-type particle filter to single-camera SLAM. We also introduce a novel partial initialization procedure that efficiently determines the depth of new landmarks. Moreover, we use information available in observations of new landmarks to improve camera pose estimates. Results show the system operating in real-time on a standard workstation while mapping hundreds of landmarks. 1.
(Show Context)

Exactly sparse delayed-state filters for view-based SLAM

by Ryan M. Eustice, Hanumant Singh, John J. Leonard - IEEE Transactions on Robotics , 2006
"... Abstract—This paper reports the novel insight that the simultaneous localization and mapping (SLAM) information matrix is exactly sparse in a delayed-state framework. Such a framework is used in view-based representations of the environment that rely upon scan-matching raw sensor data to obtain virt ..."
Abstract - Cited by 105 (21 self) - Add to MetaCart
Abstract—This paper reports the novel insight that the simultaneous localization and mapping (SLAM) information matrix is exactly sparse in a delayed-state framework. Such a framework is used in view-based representations of the environment that rely upon scan-matching raw sensor data to obtain virtual observations of robot motion with respect to a place it has previously been. The exact sparseness of the delayed-state information matrix is in contrast to other recent feature-based SLAM information algorithms, such as sparse extended information filter or thin junction-tree filter, since these methods have to make approximations in order to force the feature-based SLAM information matrix to be sparse. The benefit of the exact sparsity of the delayed-state framework is that it allows one to take advantage of the information space parameterization without incurring any sparse approximation error. Therefore, it can produce equivalent results to the full-covariance solution. The approach is validated experimentally using monocular imagery for two datasets: a test-tank experiment with ground truth, and a remotely operated vehicle survey of the RMS Titanic. Index Terms—Information filters, Kalman filtering, machine vision, mobile robot motion planning, mobile robots, recursive estimation, robot vision systems, simultaneous localization and mapping (SLAM), underwater vehicles. I.
(Show Context)

Multi-Level Surface Maps for Outdoor Terrain Mapping and Loop Closing

by Rudolph Triebel, Patrick Pfaff, Wolfram Burgard - In Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS ’06 , 2006
"... Abstract — To operate outdoors or on non-flat surfaces, mobile robots need appropriate data structures that provide a compact representation of the environment and at the same time support important tasks such as path planning and localization. One such representation that has been frequently used i ..."
Abstract - Cited by 85 (20 self) - Add to MetaCart
Abstract — To operate outdoors or on non-flat surfaces, mobile robots need appropriate data structures that provide a compact representation of the environment and at the same time support important tasks such as path planning and localization. One such representation that has been frequently used in the past are elevation maps which store in each cell of a discrete grid the height of the surface in the corresponding area. Whereas elevation maps provide a compact representation, they lack the ability to represent vertical structures or even multiple levels. In this paper, we propose a new representation denoted as multi-level surface maps (MLS maps). Our approach allows to store multiple surfaces in each cell of the grid. This enables a mobile robot to model environments with structures like bridges, underpasses, buildings or mines. Additionally, they allow to represent vertical structures. Throughout this paper we present algorithms for updating these maps based on sensory input, to match maps calculated from two different scans, and to solve the loop-closing problem given such maps. Experiments carried out with a real robot in an outdoor environment demonstrate that our approach is well-suited for representing large-scale outdoor environments. I.
(Show Context)

Fast iterative alignment of pose graphs with poor initial estimates

by Edwin Olson, John Leonard, Seth Teller - In IEEE Intl. Conf. on Robotics and Automation (ICRA , 2006
"... Abstract — A robot exploring an environment can estimate its own motion and the relative positions of features in the environment. Simultaneous Localization and Mapping (SLAM) algorithms attempt to fuse these estimates to produce a map and a robot trajectory. The constraints are generally non-linear ..."
Abstract - Cited by 81 (9 self) - Add to MetaCart
Abstract — A robot exploring an environment can estimate its own motion and the relative positions of features in the environment. Simultaneous Localization and Mapping (SLAM) algorithms attempt to fuse these estimates to produce a map and a robot trajectory. The constraints are generally non-linear, thus SLAM can be viewed as a non-linear optimization problem. The optimization can be difficult, due to poor initial estimates arising from odometry data, and due to the size of the state space. We present a fast non-linear optimization algorithm that rapidly recovers the robot trajectory, even when given a poor initial estimate. Our approach uses a variant of Stochastic Gradient Descent on an alternative state-space representation that has good stability and computational properties. We compare our algorithm to several others, using both real and synthetic data sets.
(Show Context)

Cooperative Localization in Wireless Networks

by Henk Wymeersch, Jaime Lien, Moe Z. Win
"... Location-aware technologies will revolutionize many aspects of commercial, public service, and military sectors and are expected to spawn numerous unforeseen applications. A new era of highly accurate ubiquitous location-awareness is on the horizon, enabled by a paradigm of cooperation between node ..."
Abstract - Cited by 78 (22 self) - Add to MetaCart
Location-aware technologies will revolutionize many aspects of commercial, public service, and military sectors and are expected to spawn numerous unforeseen applications. A new era of highly accurate ubiquitous location-awareness is on the horizon, enabled by a paradigm of cooperation between nodes. In this paper, we give an overview of cooperative localization approaches and apply them to ultra-wide bandwidth (UWB) wireless networks. UWB transmission technology is particularly attractive for short- to medium-range localization, especially in GPS-denied environments; wide transmission bandwidths enable robust communication in dense multi-path scenarios, and the ability to resolve sub-nanosecond delays results in centimeterlevel distance resolution. We will describe several cooperative localization algorithms and quantify their performance, based on realistic UWB ranging models developed through an extensive measurement campaign using FCC-compliant UWB radios. We will also present a powerful localization algorithm by mapping a graphical model for statistical inference onto the network topology, which results in a net-factor graph, and by developing a suitable net-message passing schedule. The resulting algorithm (SPAWN) is fully distributed, can cope with a wide variety of scenarios, and requires little communication overhead to achieve accurate and robust localization.

Visually navigating the RMS Titanic with SLAM information filters

by Ryan Eustice, Hanumant Singh, Woods Hole, Woods Hole - in Proceedings of Robotics: Science and Systems , 2005
"... Abstract — This paper describes a vision-based, large-area, simultaneous localization and mapping (SLAM) algorithm that respects the low-overlap imagery constraints typical of underwater vehicles while exploiting the inertial sensor information that is routinely available on such platforms. We prese ..."
Abstract - Cited by 75 (12 self) - Add to MetaCart
Abstract — This paper describes a vision-based, large-area, simultaneous localization and mapping (SLAM) algorithm that respects the low-overlap imagery constraints typical of underwater vehicles while exploiting the inertial sensor information that is routinely available on such platforms. We present a novel strategy for efficiently accessing and maintaining consistent covariance bounds within a SLAM information filter, thereby greatly increasing the reliability of data association. The technique is based upon solving a sparse system of linear equations coupled with the application of constant-time Kalman updates. The method is shown to produce consistent covariance estimates suitable for robot planning and data association. Real-world results are presented for a vision-based 6-DOF SLAM implementation using data from a recent ROV survey of the wreck of the RMS Titanic. I.
(Show Context)

Multi-robot Simultaneous Localization and Mapping using Particle Filters

by Andrew Howard - International Journal of Robotics Research , 2006
"... Abstract — This paper describes an on-line algorithm for multirobot simultaneous localization and mapping (SLAM). We take as our starting point the single-robot Rao-Blackwellized particle filter described in [1] and make two key generalizations. First, we extend the particle filter to handle multi-r ..."
Abstract - Cited by 73 (0 self) - Add to MetaCart
Abstract — This paper describes an on-line algorithm for multirobot simultaneous localization and mapping (SLAM). We take as our starting point the single-robot Rao-Blackwellized particle filter described in [1] and make two key generalizations. First, we extend the particle filter to handle multi-robot SLAM problems in which the initial pose of the robots is known (such as occurs when all robots start from the same location). Second, we introduce an approximation to solve the more general problem in which the initial pose of robots is not known a priori (such as occurs when the robots start from widely separated locations). In this latter case, we assume that pairs of robots will eventually ‘bump into’ one another, thereby determining their relative pose. We use this relative pose to initialize the filter, and combine the subsequent (and prior) observations from both robots into a common map. This algorithm has been experimentally validated using data from a team of four robots equipped with odometry and scanning laser range-finders. I.
(Show Context)