Person:
Dellaert, Frank

Permanent Link
https://hdl.handle.net/1853/71202
Associated Organization(s)
Organizational Unit
ORCID
ArchiveSpace Name Record

Filters

2006 14
2
14
14
Reset filters

Settings
Author: Dellaert, Frank × End date: 2006 × Start date: 2006 ×

Publication Search Results

Now showing 1 - 10 of 14
Thumbnail Image
Item

MCMC Data Association and Sparse Factorization Updating for Real Time Multitarget Tracking with Merged and Multiple Measurements

2006-12 , Khan, Zia , Balch, Tucker , Dellaert, Frank

In several multitarget tracking applications, a target may return more than one measurement per target and interacting targets may return multiple merged measurements between targets. Existing algorithms for tracking and data association, initially applied to radar tracking, do not adequately address these types of measurements. Here, we introduce a probabilistic model for interacting targets that addresses both types of measurements simultaneously. We provide an algorithm for approximate inference in this model using a Markov chain Monte Carlo (MCMC)-based auxiliary variable particle filter. We Rao-Blackwellize the Markov chain to eliminate sampling over the continuous state space of the targets. A major contribution of this work is the use of sparse least squares updating and downdating techniques, which significantly reduce the computational cost per iteration of the Markov chain. Also, when combined with a simple heuristic, they enable the algorithm to correctly focus computation on interacting targets. We include experimental results on a challenging simulation sequence. We test the accuracy of the algorithm using two sensor modalities, video, and laser range data. We also show the algorithm exhibits real time performance on a conventional PC.

View
Thumbnail Image
Item

How A.I. and multi-robot systems research will accelerate our understanding of social animal behavior

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.

View
Thumbnail Image
Item

Line-Based Structure From Motion for Urban Environments

2006-06 , Schindler, Grant , Krishnamurthy, Panchapagesan , Dellaert, Frank

We present a novel method for recovering the 3D-line structure of a scene from multiple widely separated views. Traditional optimization-based approaches to line-based structure from motion minimize the error between measured line segments and the projections of corresponding 3D lines. In such a case, 3D lines can be optimized using a minimum of 4 parameters. We show that this number of parameters can be further reduced by introducing additional constraints on the orientations of lines in a 3D scene. In our approach, 2D-lines are automatically detected in images with the assistance of an EM-based vanishing point estimation method which assumes the existence of edges along mutally orthogonal vanishing directions. Each detected line is automatically labeled with the orientation (e.g. vertical, horizontal) of the 3D line which generated the measurement, and it is this additional knowledge that we use to reduce the number of degrees of freedom of 3D lines during optimization. We present 3D reconstruction results for urban scenes based on manually established feature correspondences across images.

View
No Thumbnail Available
Item

On-line Learning of the Traversability of Unstructured Terrain for Outdoor Robot Navigation

2006 , Oh, Sang Min , Rehg, James M. , Dellaert, Frank

View
Thumbnail Image
Item

Stereo Tracking and Three-Point/One-Point Algorithms - A Robust Approach in Visual Odometry

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.

View
No Thumbnail Available
Item

Rao-Blackwellized Importance Sampling of Camera Parameters from Simple User Input with Visibility Preprocessing in Line Space

2006-06 , Quennesson, Kevin , Dellaert, Frank

Users know what they see before where they are: it is more natural to talk about high level visibility information ("I see such object") than about one's location or orientation. In this paper we introduce a method to find in 3D worlds a density of viewpoints of camera locations from high level visibility constraints on objects in this world. Our method is based on Rao-Blackwellized importance sampling. For efficiency purposes, the proposal distribution used for sampling is extracted from a visibility preprocessing technique adapted from computer graphics. We apply the method for finding in a 3D city model of Atlanta the virtual locations of real-world cameras and viewpoints.

View
Thumbnail Image
Item

A Rao-Blackwellized Particle Filter for Topological Mapping

2006-05 , Ranganathan, Ananth , Dellaert, Frank

We present a particle filtering algorithm to construct topological maps of an uninstrument environment. The algorithm presented here constructs the posterior on the space of all possible topologies given measurements, and is based on our previous work on a Bayesian inference framework for topological maps [21]. Constructing the posterior solves the perceptual aliasing problem in a general, robust manner. The use of a Rao-Blackwellized Particle Filter (RBPF) for this purpose makes the inference in the space of topologies incremental and run in real-time. The RBPF maintains the joint posterior on topological maps and locations of landmarks. We demonstrate that, using the landmark locations thus obtained, the global metric map can be obtained from the topological map generated by our algorithm through a simple post-processing step. A data-driven proposal is provided to overcome the degeneracy problem inherent in particle filters. The use of a Dirichlet process prior on landmark labels is also a novel aspect of this work. We use laser range scan and odometry measurements to present experimental results on a robot.

View
Thumbnail Image
Item

Exploiting Locality in SLAM by Nested Dissection

2006-08 , Krauthausen, Peter , Kipp, Alexander , Dellaert, Frank

The computational complexity of SLAM is dominated by the cost of factorizing a matrix derived from the measurements into a square root form, which has cubic complexity in the worst case. However, the matrices associated with the full SLAM problem are typically very sparse, as opposed to the dense problems one obtains in a filtering context. Hence much faster, sparse factorization algorithms can be used. Furthermore, the cost can be further reduced by choosing a good order in which to eliminate variables during the factorization process, leading to more or less fill-in. In particular, in this paper we investigate how a nested dissection ordering method can provably improve the performance of the full SLAM algorithm. We show that the computational complexity for the factorization of a large class of measurement matrices occurring in the SLAM problem can be tightly bound under reasonable assumptions.

View
Thumbnail Image
Item

Parameterized Duration Modeling for Switching Linear Dynamic Systems

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.

View
Thumbnail Image
Item

Bayesian Inference in the Space of Topological Maps

2006-02 , Ranganathan, Ananth , Menegatti, Emanuele , Dellaert, Frank

While probabilistic techniques have previously been investigated extensively for performing inference over the space of metric maps, no corresponding general purpose methods exist for topological maps. We present the concept of Probabilistic Topological Maps (PTMs), a sample-based representation that approximates the posterior distribution over topologies given available sensor measurements. We show that the space of topologies is equivalent to the intractably large space of set partitions on the set of available measurements. The combinatorial nature of the problem is overcome by computing an approximate, sample-based representation of the posterior. The PTM is obtained by performing Bayesian inference over the space of all possible topologies and provides a systematic solution to the problem of perceptual aliasing in the domain of topological mapping. In this paper, we describe a general framework for modeling measurements, and the use of a Markov chain Monte Carlo (MCMC) algorithm that uses specific instances of these models for odometry and appearance measurements to estimate the posterior distribution. We present experimental results that validate our technique and generate good maps when using odometry and appearance, derived from panoramic images, as sensor measurements.

View