Dellaert, Frank

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Now showing 1 - 10 of 56
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    Information-based Reduced Landmark SLAM
    (Georgia Institute of Technology, 2015-05) Choudhary, Siddharth ; Indelman, Vadim ; Christensen, Henrik I. ; Dellaert, Frank
    In this paper, we present an information-based approach to select a reduced number of landmarks and poses for a robot to localize itself and simultaneously build an accurate map. We develop an information theoretic algorithm to efficiently reduce the number of landmarks and poses in a SLAM estimate without compromising the accuracy of the estimated trajectory. We also propose an incremental version of the reduction algorithm which can be used in SLAM framework resulting in information based reduced landmark SLAM. The results of reduced landmark based SLAM algorithm are shown on Victoria park dataset and a Synthetic dataset and are compared with standard graph SLAM (SAM [6]) algorithm. We demonstrate a reduction of 40-50% in the number of landmarks and around 55% in the number of poses with minimal estimation error as compared to standard SLAM algorithm.
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    Incremental Light Bundle Adjustment for Robotics Navigation
    (Georgia Institute of Technology, 2013-11) Indelman, Vadim ; Melim, Andrew ; Dellaert, Frank
    This paper presents a new computationally-efficient method for vision-aided navigation (VAN) in autonomous robotic applications. While many VAN approaches are capable of processing incoming visual observations, incorporating loop-closure measurements typically requires performing a bundle adjustment (BA) optimization, that involves both all the past navigation states and the observed 3D points. Our approach extends the incremental light bundle adjustment (LBA) method, recently developed for structure from motion [10], to information fusion in robotics navigation and in particular for including loop-closure information. Since in many robotic applications the prime focus is on navigation rather then mapping, and as opposed to traditional BA, we algebraically eliminate the observed 3D points and do not explicitly estimate them. Computational complexity is further improved by applying incremental inference. To maintain high-rate performance over time, consecutive IMU measurements are summarized using a recently-developed technique and navigation states are added to the optimization only at camera rate. If required, the observed 3D points can be reconstructed at any time based on the optimized robot’s poses. The proposed method is compared to BA both in terms of accuracy and computational complexity in a statistical simulation study.
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    Support-Theoretic Subgraph Preconditioners for Large-Scale SLAM
    (Georgia Institute of Technology, 2013-11) Jian, Yong-Dian ; Balcan, Doru ; Panageas, Ioannis ; Tetali, Prasad ; Dellaert, Frank
    Efficiently solving large-scale sparse linear systems is important for robot mapping and navigation. Recently, the subgraph-preconditioned conjugate gradient method has been proposed to combine the advantages of two reigning paradigms, direct and iterative methods, to improve the efficiency of the solver. Yet the question of how to pick a good subgraph is still open. In this paper, we propose a new metric to measure the quality of a spanning tree preconditioner based on support theory. We use this metric to develop an algorithm to find good subgraph preconditioners and apply them to solve the SLAM problem. The results show that although the proposed algorithm is not fast enough, the new metric is effective and resulting subgraph preconditioners significantly improve the efficiency of the state-of-the-art solver.
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    DDF-SAM 2.0: Consistent Distributed Smoothing and Mapping
    (Georgia Institute of Technology, 2013-05) Cunningham, Alexander ; Indelman, Vadim ; Dellaert, Frank
    This paper presents an consistent decentralized data fusion approach for robust multi-robot SLAM in dan- gerous, unknown environments. The DDF-SAM 2.0 approach extends our previous work by combining local and neigh- borhood information in a single, consistent augmented local map, without the overly conservative approach to avoiding information double-counting in the previous DDF-SAM algo- rithm. We introduce the anti-factor as a means to subtract information in graphical SLAM systems, and illustrate its use to both replace information in an incremental solver and to cancel out neighborhood information from shared summarized maps. This paper presents and compares three summarization techniques, with two exact approaches and an approximation. We evaluated the proposed system in a synthetic example and show the augmented local system and the associated summarization technique do not double-count information, while keeping performance tractable.
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    Path Planning with Uncertainty: Voronoi Uncertainty Fields
    (Georgia Institute of Technology, 2013-05) Ok, Kyel ; Ansari, Sameer ; Gallagher, Billy ; Sica, William ; Dellaert, Frank ; Stilman, Mike
    In this paper, a two-level path planning algorithm that deals with map uncertainty is proposed. The higher level planner uses modified generalized Voronoi diagrams to guarantee finding a connected path from the start to the goal if a collision-free path exists. The lower level planner considers uncertainty of the observed obstacles in the environment and assigns repulsive forces based on their distance to the robot and their positional uncertainty. The attractive forces from the Voronoi nodes and the repulsive forces from the uncertainty- biased potential fields form a hybrid planner we call Voronoi Uncertainty Fields (VUF). The proposed planner has two strong properties: (1) bias against uncertain obstacles, and (2) completeness. We analytically prove the properties and run simulations to validate our method in a forest-like environment.
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    iSAM2: Incremental Smoothing and Mapping with Fluid Relinearization and Incremental Variable Reordering
    (Georgia Institute of Technology, 2011) Kaess, Michael ; Johannsson, Hordur ; Roberts, Richard ; Ila, Viorela ; Leonard, John ; Dellaert, Frank
    We present iSAM2, a fully incremental, graphbased version of incremental smoothing and mapping (iSAM). iSAM2 is based on a novel graphical model-based interpretation of incremental sparse matrix factorization methods, afforded by the recently introduced Bayes tree data structure. The original iSAM algorithm incrementally maintains the square root information matrix by applying matrix factorization updates. We analyze the matrix updates as simple editing operations on the Bayes tree and the conditional densities represented by its cliques. Based on that insight, we present a new method to incrementally change the variable ordering which has a large effect on efficiency. The efficiency and accuracy of the new method is based on fluid relinearization, the concept of selectively relinearizing variables as needed. This allows us to obtain a fully incremental algorithm without any need for periodic batch steps. We analyze the properties of the resulting algorithm in detail, and show on various real and simulated datasets that the iSAM2 algorithm compares favorably with other recent mapping algorithms in both quality and efficiency.
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    Visibility Learning in Large-Scale Urban Environment
    (Georgia Institute of Technology, 2011) Alcantarilla, Pablo F. ; Ni, Kai ; Bergasa, Luis M. ; Dellaert, Frank
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    Effects of sensory precision on mobile robot localization and mapping
    (Georgia Institute of Technology, 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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    3D Reconstruction of Underwater Structures
    (Georgia Institute of Technology, 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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    Learning Visibility of Landmarks for Vision-Based Localization
    (Georgia Institute of Technology, 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.