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Dellaert, Frank

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Author: Cunningham, Alexander × End date: 2020 × Start date: 2010 × search.filters.applied.f.resourcesubtype: Proceedings ×

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Now showing 1 - 7 of 7
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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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    Consistent Decentralized Graphical SLAM with Anti-Factor Down-Dating
    (Georgia Institute of Technology, 2012-11) Cunningham, Alexander ; Indelman, Vadim ; Dellaert, Frank
    This report presents our recent and ongoing work developing a consistent decentralized data fusion approach for robust multi-robot SLAM in dangerous, unknown environments. The DDF-SAM 2.0 approach extends our previous work by combining local and neighborhood information in a single, consistent augmented local map, without the overly conservative to avoiding information double-counting in the previous DDF-SAM approach. 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. Evaluations in a synthetic example environment demonstrate that we avoid double-counting information.
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    Accurate On-Line 3D Occupancy Grids Using Manhattan World Constraints
    (Georgia Institute of Technology, 2012-10) Peasley, Brian ; Birchfield, Stan ; Cunningham, Alexander ; Dellaert, Frank
    In this paper we present an algorithm for constructing nearly drift-free 3D occupancy grids of large indoor environments in an online manner. Our approach combines data from an odometry sensor with output from a visual registration algorithm, and it enforces a Manhattan world constraint by utilizing factor graphs to produce an accurate online estimate of the trajectory of a mobile robotic platform. We also examine the advantages and limitations of the octree data structure representation of a 3D environment. Through several experiments in environments with varying sizes and construction we show that our method reduces rotational and translational drift significantly without performing any loop closing techniques.
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    Large Scale Experimental Design for Decentralized SLAM
    (Georgia Institute of Technology, 2012-05-01) Cunningham, Alexander ; Dellaert, Frank
    This paper presents an analysis of large scale decentralized SLAM under a variety of experimental conditions to illustrate design trade-o s relevant to multi-robot mapping in challenging environments. As a part of work through the MAST CTA, the focus of these robot teams is on the use of small-scale robots with limited sensing, communication and computational resources. To evaluate mapping algorithms with large numbers (50+) of robots, we developed a simulation incorporating sensing of unlabeled landmarks, line-of-sight blocking obstacles, and communication modeling. Scenarios are randomly generated with variable models for sensing, communication, and robot behavior. The underlying Decentralized Data Fusion (DDF) algorithm in these experiments enables robots to construct a map of their surroundings by fusing local sensor measurements with condensed map information from neighboring robots. Each robot maintains a cache of previously collected condensed maps from neighboring robots, and actively distributes these maps throughout the network to ensure resilience to communication and node failures. We bound the size of the robot neighborhoods to control the growth of the size of neighborhood maps. We present the results of experiments conducted in these simulated scenarios under varying measurement models and conditions while measuring mapping performance. We discuss the trade-o s between mapping performance and scenario design, including robot teams separating and joining, multi-robot data association, exploration bounding, and neighborhood sizes.
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    Fully Distributed Scalable Smoothing and Mapping with Robust Multi-robot Data Association
    (Georgia Institute of Technology, 2012-05) Cunningham, Alexander ; Wurm, Kai M. ; Burgard, Wolfram ; Dellaert, Frank
    In this paper we focus on the multi-robot perception problem, and present an experimentally validated endto- end multi-robot mapping framework, enabling individual robots in a team to see beyond their individual sensor horizons. The inference part of our system is the DDF-SAM algorithm [1], which provides a decentralized communication and inference scheme, but did not address the crucial issue of data association. One key contribution is a novel, RANSAC-based, approach for performing the between-robot data associations and initialization of relative frames of reference. We demonstrate this system with both data collected from real robot experiments, as well as in a large scale simulated experiment demonstrating the scalability of the proposed approach.
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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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    DDF-SAM: Fully Distributed SLAM using Constrained Factor Graphs
    (Georgia Institute of Technology, 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.