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505
Dynamic Bayesian Networks: Representation, Inference and Learning
, 2002
"... Modelling sequential data is important in many areas of science and engineering. Hidden Markov models (HMMs) and Kalman filter models (KFMs) are popular for this because they are simple and flexible. For example, HMMs have been used for speech recognition and bio-sequence analysis, and KFMs have bee ..."
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Cited by 770 (3 self)
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Modelling sequential data is important in many areas of science and engineering. Hidden Markov models (HMMs) and Kalman filter models (KFMs) are popular for this because they are simple and flexible. For example, HMMs have been used for speech recognition and bio-sequence analysis, and KFMs have been used for problems ranging from tracking planes and missiles to predicting the economy. However, HMMs
and KFMs are limited in their “expressive power”. Dynamic Bayesian Networks (DBNs) generalize HMMs by allowing the state space to be represented in factored form, instead of as a single discrete random variable. DBNs generalize KFMs by allowing arbitrary probability distributions, not just (unimodal) linear-Gaussian. In this thesis, I will discuss how to represent many different kinds of models as DBNs, how to perform exact and approximate inference in DBNs, and how to learn DBN models from sequential data.
In particular, the main novel technical contributions of this thesis are as follows: a way of representing
Hierarchical HMMs as DBNs, which enables inference to be done in O(T) time instead of O(T 3), where T is the length of the sequence; an exact smoothing algorithm that takes O(log T) space instead of O(T); a simple way of using the junction tree algorithm for online inference in DBNs; new complexity bounds on exact online inference in DBNs; a new deterministic approximate inference algorithm called factored frontier; an analysis of the relationship between the BK algorithm and loopy belief propagation; a way of
applying Rao-Blackwellised particle filtering to DBNs in general, and the SLAM (simultaneous localization
and mapping) problem in particular; a way of extending the structural EM algorithm to DBNs; and a variety of different applications of DBNs. However, perhaps the main value of the thesis is its catholic presentation of the field of sequential data modelling.
Mobile Robot Localization and Mapping with Uncertainty using Scale-Invariant Visual Landmarks
, 2002
"... A key component of a mobile robot system is the ability to localize itself accurately and, simultaneously, to build a map of the environment. Most of the existing algorithms are based on laser range finders, sonar sensors or artificial landmarks. In this paper, we describe a vision-based mobile robo ..."
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Cited by 279 (12 self)
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A key component of a mobile robot system is the ability to localize itself accurately and, simultaneously, to build a map of the environment. Most of the existing algorithms are based on laser range finders, sonar sensors or artificial landmarks. In this paper, we describe a vision-based mobile robot localization and mapping algorithm, which uses scale-invariant image features as natural landmarks in unmodified environments. The invariance of these features to image translation, scaling and rotation makes them suitable landmarks for mobile robot localization and map building. With our Triclops stereo vision system, these landmarks are localized and robot ego-motion is estimated by least-squares minimization of the matched landmarks. Feature viewpoint variation and occlusion are taken into account by maintaining a view direction for each landmark. Experiments show that these visual landmarks are robustly matched, robot pose is estimated and a consistent three-dimensional map is built. As image features are not noise-free, we carry out error analysis for the landmark positions and the robot pose. We use Kalman filters to track these landmarks in a dynamic environment, resulting in a database map with landmark positional uncertainty.
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 ..."
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Cited by 179 (30 self)
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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 limited-size 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.
An Atlas Framework for Scalable Mapping
- in IEEE International Conference on Robotics and Automation
, 2003
"... This paper describes Atlas, a hybrid metrical /topological approach to SLAM that achieves efficient mapping of large-scale environments. The representation is a graph of coordinate frames, with each vertex in the graph representing a local frame, and each edge representing the transformation between ..."
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Cited by 178 (19 self)
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This paper describes Atlas, a hybrid metrical /topological approach to SLAM that achieves efficient mapping of large-scale environments. The representation is a graph of coordinate frames, with each vertex in the graph representing a local frame, and each edge representing the transformation between adjacent frames. In each frame, we build a map that captures the local environment and the current robot pose along with the uncertainties of each. Each map's uncertainties are modeled with respect to its own frame. Probabilities of entities with respect to arbitrary frames are generated by following a path formed by the edges between adjacent frames, computed via Dijkstra's shortest path algorithm. Loop closing is achieved via an efficient map matching algorithm. We demonstrate the technique running in real-time in a large indoor structured environment (2.2 km path length) with multiple nested loops using laser or ultrasonic ranging sensors.
A computationally efficient method for large-scale concurrent mapping and localization
, 2000
"... Decoupled stochastic mapping (DSM) is a computationally efficient approach to large-scale concurrent mapping and localization. DSM reduces the computational burden of conventional stochastic mapping by dividing the environment into multiple overlapping submap regions, each with its own stochastic ma ..."
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Cited by 161 (8 self)
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Decoupled stochastic mapping (DSM) is a computationally efficient approach to large-scale concurrent mapping and localization. DSM reduces the computational burden of conventional stochastic mapping by dividing the environment into multiple overlapping submap regions, each with its own stochastic map. Two new approximation techniques are utilized for transferring vehicle state information from one submap to another, yielding a constant-time algorithm whose memory requirements scale linearly with the size of the operating area. The performance of two different variations of the algorithm is demonstrated through simulations of environments with 110 and 1200 features. Experimental results are presented for an environment with 93 features using sonar data obtained in a 3 by 9 by 1 meter testing tank.
Adapting the Sample Size in Particle Filters Through KLD-Sampling
- International Journal of Robotics Research
, 2003
"... Over the last years, particle filters have been applied with great success to a variety of state estimation problems. In this paper we present a statistical approach to increasing the efficiency of particle filters by adapting the size of sample sets during the estimation process. ..."
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Cited by 150 (9 self)
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Over the last years, particle filters have been applied with great success to a variety of state estimation problems. In this paper we present a statistical approach to increasing the efficiency of particle filters by adapting the size of sample sets during the estimation process.
FrameSLAM: From bundle adjustment to real-time visual mapping
- IEEE Trans. on Robotics
, 2008
"... Abstract—Many successful indoor mapping techniques employ frame-to-frame matching of laser scans to produce detailed local maps as well as the closing of large loops. In this paper, we propose a framework for applying the same techniques to visual imagery. We match visual frames with large numbers o ..."
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Cited by 147 (5 self)
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Abstract—Many successful indoor mapping techniques employ frame-to-frame matching of laser scans to produce detailed local maps as well as the closing of large loops. In this paper, we propose a framework for applying the same techniques to visual imagery. We match visual frames with large numbers of point features, using classic bundle adjustment techniques from computational vision, but we keep only relative frame pose information (a skeleton). The skeleton is a reduced nonlinear system that is a faithful approx-imation of the larger system and can be used to solve large loop closures quickly, as well as forming a backbone for data association and local registration. We illustrate the workings of the system with large outdoor datasets (10 km), showing large-scale loop closure and precise localization in real time. Index Terms—Visual mapping, visual odometry, visual SLAM. I.
Square Root SAM: Simultaneous localization and mapping via square root information smoothing
, 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 ..."
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Cited by 144 (38 self)
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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.
Exactly sparse delayed-state filters for view-based SLAM
- 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 ..."
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Cited by 105 (21 self)
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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.
