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Dellaert, Frank

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End date: 2010 × Start date: 2002 × Type: Text ×

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Now showing 1 - 10 of 98
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The Bayes Tree: An Algorithmic Foundation for Probabilistic Robot Mapping

2010-12 , Kaess, Michael , Ila, Viorela , Roberts, Richard , Dellaert, Frank

We present a novel data structure, the Bayes tree, that provides an algorithmic foundation enabling a better understanding of existing graphical model inference algorithms and their connection to sparse matrix factorization methods. Similar to a clique tree, a Bayes tree encodes a factored probability density, but unlike the clique tree it is directed and maps more naturally to the square root information matrix of the simultaneous localization and mapping (SLAM) problem. In this paper, we highlight three insights provided by our new data structure. First, the Bayes tree provides a better understanding of batch matrix factorization in terms of probability densities. Second, we show how the fairly abstract updates to a matrix factorization translate to a simple editing of the Bayes tree and its conditional densities. Third, we apply the Bayes tree to obtain a completely novel algorithm for sparse nonlinear incremental optimization, that combines incremental updates with fluid relinearization of a reduced set of variables for efficiency, combined with fast convergence to the exact solution. We also present a novel strategy for incremental variable reordering to retain sparsity.We evaluate our algorithm on standard datasets in both landmark and pose SLAM settings.

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The Bayes Tree: Enabling Incremental Reordering and Fluid Relinearization for Online Mapping

2010-01-29 , Kaess, Michael , Ila, Viorela , Roberts, Richard , Dellaert, Frank

In this paper we present a novel data structure, the Bayes tree, which exploits the connections between graphical model inference and sparse linear algebra. The proposed data structure provides a new perspective on an entire class of simultaneous localization and mapping (SLAM) algorithms. Similar to a junction tree, a Bayes tree encodes a factored probability density, but unlike the junction tree it is directed and maps more naturally to the square root information matrix of the SLAM problem. This makes it eminently suited to encode the sparse nature of the problem, especially in a smoothing and mapping (SAM) context. The inherent sparsity of SAM has already been exploited in the literature to produce efficient solutions in both batch and online mapping. The graphical model perspective allows us to develop a novel incremental algorithm that seamlessly incorporates reordering and relinearization. This obviates the need for expensive periodic batch operations from previous approaches, which negatively affect the performance and detract from the intended online nature of the algorithm. The new method is evaluated using simulated and real-world datasets in both landmark and pose SLAM settings.

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Learning Visibility of Landmarks for Vision-Based Localization

2010 , Alcantarilla, Pablo F. , Oh, Sang Min , Mariottini, Gian Luca , Bergasa, Luis M. , Dellaert, Frank

We aim to perform robust and fast vision-based localization using a pre-existing large map of the scene. A key step in localization is associating the features extracted from the image with the map elements at the current location. Although the problem of data association has greatly benefited from recent advances in appearance-based matching methods, less attention has been paid to the effective use of the geometric relations between the 3D map and the camera in the matching process. In this paper we propose to exploit the geometric relationship between the 3D map and the camera pose to determine the visibility of the features. In our approach, we model the visibility of every map feature w.r.t. the camera pose using a non-parametric distribution model. We learn these non-parametric distributions during the 3D reconstruction process, and develop efficient algorithms to predict the visibility of features during localization. With this approach, the matching process only uses those map features with the highest visibility score, yielding a much faster algorithm and superior localization results. We demonstrate an integrated system based on the proposed idea and highlight its potential benefits for the localization in large and cluttered environments.

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Multi-Level Submap Based SLAM Using Nested Dissection

2010 , Ni, Kai , Dellaert, Frank

We propose a novel batch algorithm for SLAM problems that distributes the workload in a hierarchical way. We show that the original SLAM graph can be recursively partitioned into multiple-level submaps using the nested dissection algorithm, which leads to the cluster tree, a powerful graph representation. By employing the nested dissection algorithm, our algorithm greatly minimizes the dependencies between two subtrees, and the optimization of the original SLAM graph can be done using a bottom-up inference along the corresponding cluster tree. To speed up the computation, we also introduce a base node for each submap and use it to represent the rigid transformation of the submap in the global coordinate frame. As a result, the optimization moves the base nodes rather than the actual submap variables. We demonstrate that our algorithm is not only exact but also much faster than alternative approaches in both simulations and real-world experiments.

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Effects of sensory precision on mobile robot localization and mapping

2010-12 , Rogers, John G. , Trevor, Alexander J. B. , Nieto-Granda, Carlos , Cunningham, Alexander , Paluri, Manohar , Michael, Nathan , Dellaert, Frank , Christensen, Henrik I. , Kumar, Vijay

This paper will explore the relationship between sensory accuracy and Simultaneous Localization and Mapping (SLAM) performance. As inexpensive robots are developed with commodity components, the relationship between performance level and accuracy will need to be determined. Experiments are presented in this paper which compare various aspects of sensor performance such as maximum range, noise, angular precision, and viewable angle. In addition, mapping results from three popular laser scanners (Hokuyo’s URG and UTM30, as well as SICK’s LMS291) are compared.

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Local Exponential Maps: Towards Massively Distributed Multi-robot Mapping

2010 , Dellaert, Frank , Fathi, Alireza , Cunningham, Alex , Paluri, Balmanohar , Ni, Kai

We present a novel paradigm for massively distributed, large-scale multi-robot mapping. Our goal is to explore techniques that can support continuous mapping over an indefinite amount of time. We argue that to scale to city or even global scales the concept of a single globally consistent map has to be abandoned, and present an infrastructure-supported solution where most of the inference and map-maintenance is done on local "map-servers", rather than on the robot itself. The main technical contribution in the paper is a factor-graph-based scheme for making this possible, and a novel local map representation, local exponential maps, that enable indefinite map updates while remaining self-consistent over time. We present initial experimental results both in simulation and using real data, although a full-scale deployment and evaluation of the technique is left for future work.

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DDF-SAM: Fully Distributed SLAM using Constrained Factor Graphs

2010 , Cunningham, Alexander , Paluri, Manohar , Dellaert, Frank

We address the problem of multi-robot distributed SLAM with an extended Smoothing and Mapping (SAM) approach to implement Decentralized Data Fusion (DDF). We present DDF-SAM, a novel method for efficiently and robustly distributing map information across a team of robots, to achieve scalability in computational cost and in communication bandwidth and robustness to node failure and to changes in network topology. DDF-SAM consists of three modules: (1) a local optimization module to execute single-robot SAM and condense the local graph; (2) a communication module to collect and propagate condensed local graphs to other robots, and (3) a neighborhood graph optimizer module to combine local graphs into maps describing the neighborhood of a robot. We demonstrate scalability and robustness through a simulated example, in which inference is consistently faster than a comparable naive approach.

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Probabilistic Structure Matching for Visual SLAM with a Multi-Camera Rig

2010-02 , Kaess, Michael , Dellaert, Frank

We propose to use a multi-camera rig for simultaneous localization and mapping (SLAM), providing flexibility in sensor placement on mobile robot platforms while exploiting the stronger localization constraints provided by omni-directional sensors. In this context, we present a novel probabilistic approach to data association, that takes into account that features can also move between cameras under robot motion. Our approach circumvents the combinatorial data association problem by using an incremental expectation maximization algorithm. In the expectation step we determine a distribution over correspondences by sampling. In the maximization step, we find optimal parameters of a density over the robot motion and environment structure. By summarizing the sampling results in so-called virtual measurements, the resulting optimization simplifies to the equivalent optimization problem for known correspondences. We present results for simulated data, as well as for data obtained by a mobile robot equipped with a multi-camera rig.

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3D Reconstruction of Underwater Structures

2010 , Beall, Chris , Lawrence, Brian J. , Ila, Viorela , Dellaert, Frank

Environmental change is a growing international concern, calling for the regular monitoring, studying and preserving of detailed information about the evolution of underwater ecosystems. For example, fragile coral reefs are exposed to various sources of hazards and potential destruction, and need close observation. Computer vision offers promising technologies to build 3D models of an environment from two dimensional images. The state of the art techniques have enabled high-quality digital reconstruction of large-scale structures, e.g., buildings and urban environments, but only sparse representations or dense reconstruction of small objects have been obtained from underwater video and still imagery. The application of standard 3D reconstruction methods to challenging underwater environments typically produces unsatisfactory results. Accurate, full camera trajectories are needed to serve as the basis for dense 3D reconstruction. A highly accurate sparse 3D reconstruction is the ideal foundation on which to base subsequent dense reconstruction algorithms. In our application the models are constructed from synchronized high definition videos collected using a wide baseline stereo rig. The rig can be hand-held, attached to a boat, or even to an autonomous underwater vehicle. We solve this problem by employing a smoothing and mapping toolkit developed in our lab specifically for this type of application. The result of our technique is a highly accurate sparse 3D reconstruction of underwater structures such as corals.

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EasySLAM

2010 , Fathi, Alireza , Cunningham, Alex , Paluri, Balmanohar , Ni, Kai , Dellaert, Frank

EasySLAM is a robust, accurate, efficient and easy-to-use visual SLAM framework which uses the unique properties of planar landmarks to navigate robots in societal settings. Due to the use of landmarks which can be associated with semantics, a hybrid symbolic-metric SLAM variant is obtained that makes the maps immediately usable for human-robot interaction, high-level monitoring, and semantic analysis. EasySLAM associates a set of landmarks to each part of the house (e.g. kitchen, living room, bathroom, bedroom, etc.) and takes navigation commands such as "go to kitchen". Loalization and mapping, planning and navigation results are presented with an inexpensive, commercially available robot and uniquely identifiable markers. SLAM with planar landmarks is easy, robust, and fills the real need in both research and society, and we have a system that everyone can use.

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