Person:

Dellaert, Frank

Permanent Link

https://hdl.handle.net/1853/71202

Associated Organization(s)

Organizational Unit

School of Interactive Computing

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Publication Search Results

Now showing 1 - 10 of 34

Out-of-Core Bundle Adjustment for Large-Scale 3D Reconstruction

(Georgia Institute of Technology, 2007-10) Ni, Kai ; Steedly, Drew ; Dellaert, Frank

Large-scale 3D reconstruction has recently received much attention from the computer vision community. Bundle adjustment is a key component of 3D reconstruction problems. However, traditional bundle adjustment algorithms require a considerable amount of memory and computational resources. In this paper, we present an extremely efficient, inherently out-of-core bundle adjustment algorithm. We decouple the original problem into several submaps that have their own local coordinate systems and can be optimized in parallel. A key contribution to our algorithm is making as much progress towards optimizing the global non-linear cost function as possible using the fragments of the reconstruction that are currently in core memory. This allows us to converge with very few global sweeps (often only two) through the entire reconstruction. We present experimental results on large-scale 3D reconstruction datasets, both synthetic and real.
Fast 3D Pose Estimation With Out-of-Sequence Measurements

(Georgia Institute of Technology, 2007-10) Ranganathan, Ananth ; Kaess, Michael ; Dellaert, Frank

We present an algorithm for pose estimation using fixed-lag smoothing. We show that fixed-lag smoothing enables inclusion of measurements from multiple asynchronous measurement sources in an optimal manner. Since robots usually have a plurality of uncoordinated sensors, our algorithm has an advantage over filtering-based estimation algorithms, which cannot incorporate delayed measurements optimally. We provide an implementation of the general fixed-lag smoothing algorithm using square root smoothing, a technique that has recently become prominent. Square root smoothing uses fast sparse matrix factorization and enables our fixed-lag pose estimation algorithm to run at upwards of 20 Hz. Our algorithm has been extensively tested over hundreds of hours of operation on a robot operating in outdoor environments. We present results based on these tests that verify our claims using wheel encoders, visual odometry, and GPS as sensors.
Inferring Temporal Order of Images From 3D Structure

(Georgia Institute of Technology, 2007-06) Schindler, Grant ; Dellaert, Frank ; Kang, Sing Bing

In this paper, we describe a technique to temporally sort a collection of photos that span many years. By reasoning about persistence of visible structures, we show how this sorting task can be formulated as a constraint satisfaction problem (CSP). Casting this problem as a CSP allows us to efficiently find a suitable ordering of the images despite the large size of the solution space (factorial in the number of images) and the presence of occlusions. We present experimental results for photographs of a city acquired over a one hundred year period.
iSAM: Fast Incremental Smoothing and Mapping with Efficient Data Association

(Georgia Institute of Technology, 2007-04) Kaess, Michael ; Ranganathan, Ananth ; Dellaert, Frank

We introduce incremental smoothing and mapping (iSAM), a novel approach to the problem of simultaneous localization and mapping (SLAM) that addresses the data association problem and allows real-time application in large-scale environments. We employ smoothing to obtain the complete trajectory and map without the need for any approximations, exploiting the natural sparsity of the smoothing information matrix. A QR-factorization of this information matrix is at the heart of our approach. It provides efficient access to the exact covariances as well as to conservative estimates that are used for online data association. It also allows recovery of the exact trajectory and map at any given time by backsubstitution. Instead of refactoring in each step, we update the QR-factorization whenever a new measurement arrives. We analyze the effect of loops, and show how our approach extends to the non-linear case. Finally, we provide experimental validation of the overall non-linear algorithm based on the standard Victoria Park data set with unknown correspondences.
Tectonic SAM: Exact, Out-of-Core, Submap-Based SLAM

(Georgia Institute of Technology, 2007-04) Ni, Kai ; Steedly, Drew ; Dellaert, Frank

Simultaneous localization and mapping (SLAM) is a method that robots use to explore, navigate, and map an unknown environment. However, this method poses inherent problems with regard to cost and time. To lower computation costs, smoothing and mapping (SAM) approaches have shown some promise, and they also provide more accurate solutions than filtering approaches in realistic scenarios. However, in SAM approaches, updating the linearization is still the most time-consuming step. To mitigate this problem, we propose a submap-based approach, Tectonic SAM, in which the original optimization problem is solved by using a divide-and-conquer scheme. Submaps are optimized independently and parameterized relative to a local coordinate frame. During the optimization, the global position of the submap may change dramatically, but the positions of the nodes in the submap relative to the local coordinate frame do not change very much. The key contribution of this paper is to show that the linearization of the submaps can be cached and reused when they are combined into a global map. According to the results of both simulation and real experiments, Tectonic SAM drastically speeds up SAM in very large environments while still maintaining its global accuracy.
Fast Incremental Square Root Information Smoothing

(Georgia Institute of Technology, 2007-01) Kaess, Michael ; Ranganathan, Ananth ; Dellaert, Frank

We propose a novel approach to the problem of simultaneous localization and mapping (SLAM) based on incremental smoothing, that is suitable for real-time applications in large-scale environments. The main advantages over filter-based algorithms are that we solve the full SLAM problem without the need for any approximations, and that we do not suffer from linearization errors. We achieve efficiency by updating the square-root information matrix, a factored version of the naturally sparse smoothing information matrix. We can efficiently recover the exact trajectory and map at any given time by back-substitution. Furthermore, our approach allows access to the exact covariances, as it does not suffer from under-estimation of uncertainties, which is another problem inherent to filters. We present simulation-based results for the linear case, showing constant time updates for exploration tasks. We further evaluate the behavior in the presence of loops, and discuss how our approach extends to the non-linear case. Finally, we evaluate the overall non-linear algorithm on the standard Victoria Park data set.
Loopy SAM

(Georgia Institute of Technology, 2007-01) Ranganathan, Ananth ; Kaess, Michael ; Dellaert, Frank

Smoothing approaches to the Simultaneous Localization and Mapping (SLAM) problem in robotics are superior to the more common filtering approaches in being exact, better equipped to deal with non-linearities, and computing the entire robot trajectory. However, while filtering algorithms that perform map updates in constant time exist, no analogous smoothing method is available. We aim to rectify this situation by presenting a smoothing-based solution to SLAM using Loopy Belief Propagation (LBP) that can perform the trajectory and map updates in constant time except when a loop is closed in the environment. The SLAM problem is represented as a Gaussian Markov Random Field (GMRF) over which LBP is performed. We prove that LBP, in this case, is equivalent to Gauss-Seidel relaxation of a linear system. The inability to compute marginal covariances efficiently in a smoothing algorithm has previously been a stumbling block to their widespread use. LBP enables the efficient recovery of the marginal covariances, albeit approximately, of landmarks and poses. While the final covariances are overconfident, the ones obtained from a spanning tree of the GMRF are conservative, making them useful for data association. Experiments in simulation and using real data are presented.
Stereo Tracking and Three-Point/One-Point Algorithms - A Robust Approach in Visual Odometry

(Georgia Institute of Technology, 2006-10) Ni, Kai ; Dellaert, Frank

In this paper, we present an approach of calculating visual odometry for outdoor robots equipped with a stereo rig. Instead of the typical feature matching or tracking, we use an improved stereo-tracking method that simultaneously decides the feature displacement in both cameras. Based on the matched features, a three-point algorithm for the resulting quadrifocal setting is carried out in a RANSAC framework to recover the unknown odometry. In addition, the change in rotation can be derived from infinity homography, and the remaining translational unknowns can be obtained even faster consequently . Both approaches are quite robust and deal well with challenging conditions such as wheel slippage.
How A.I. and multi-robot systems research will accelerate our understanding of social animal behavior

(Georgia Institute of Technology, 2006-07) Balch, Tucker ; Dellaert, Frank ; Feldman, Adam ; Guillory, Andrew ; Isbell, Charles L. ; Khan, Zia ; Pratt, Stephen ; Stein, Andrew ; Wilde, Hank

Our understanding of social insect behavior has significantly influenced A.I. and multi-robot systems’ research (e.g. ant algorithms and swarm robotics). In this work, however, we focus on the opposite question, namely: “how can multi-robot systems research contribute to the understanding of social animal behavior?.” As we show, we are able to contribute at several levels: First, using algorithms that originated in the robotics community, we can track animals under observation to provide essential quantitative data for animal behavior research. Second, by developing and applying algorithms originating in speech recognition and computer vision, we can automatically label the behavior of animals under observation. Our ultimate goal, however, is to automatically create, from observation, executable models of behavior. An executable model is a control program for an agent that can run in simulation (or on a robot). The representation for these executable models is drawn from research in multi-robot systems programming. In this paper we present the algorithms we have developed for tracking, recognizing, and learning models of social animal behavior, details of their implementation, and quantitative experimental results using them to study social insects.
Parameterized Duration Modeling for Switching Linear Dynamic Systems

(Georgia Institute of Technology, 2006-06) Oh, Sang Min ; Rehg, James M. ; Dellaert, Frank

We introduce an extension of switching linear dynamic systems (SLDS) with parameterized duration modeling capabilities. The proposed model allows arbitrary duration models and overcomes the limitation of a geometric distribution induced in standard SLDSs. By incorporating a duration model which reflects the data more closely, the resulting model provides reliable inference results which are robust against observation noise. Moreover, existing inference algorithms for SLDSs can be adopted with only modest additional effort in most cases where an SLDS model can be applied. In addition, we observe the fact that the duration models would vary across data sequences in certain domains, which complicates learning and inference tasks. Such variability in duration is overcome by introducing parameterized duration models. The experimental results on honeybee dance decoding tasks demonstrate the robust inference capabilities of the proposed model.