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 11

Bayesian Surprise and Landmark Detection

(Georgia Institute of Technology, 2009-05) Ranganathan, Ananth ; Dellaert, Frank

Automatic detection of landmarks, usually special places in the environment such as gateways, for topological mapping has proven to be a difficult task. We present the use of Bayesian surprise, introduced in computer vision, for landmark detection. Further, we provide a novel hierarchical, graphical model for the appearance of a place and use this model to perform surprise-based landmark detection. Our scheme is agnostic to the sensor type, and we demonstrate this by implementing a simple laser model for computing surprise. We evaluate our landmark detector using appearance and laser measurements in the context of a topological mapping algorithm, thus demonstrating the practical applicability of the detector.
Place Recognition-Based Fixed-Lag Smoothing for Environments with Unreliable GPS

(Georgia Institute of Technology, 2008-05) Mottaghi, Roozbeh ; Kaess, Michael ; Ranganathan, Ananth ; Roberts, Richard ; Dellaert, Frank

Pose estimation of outdoor robots presents some distinct challenges due to the various uncertainties in the robot sensing and action. In particular, global positioning sensors of outdoor robots do not always work perfectly, causing large drift in the location estimate of the robot. To overcome this common problem, we propose a new approach for global localization using place recognition. First, we learn the location of some arbitrary key places using odometry measurements and GPS measurements only at the start and the end of the robot trajectory. In subsequent runs, when the robot perceives a key place, our fixed-lag smoother fuses odometry measurements with the relative location to the key place to improve its pose estimate. Outdoor mobile robot experiments show that place recognition measurements significantly improve the estimate of the smoother in the absence of GPS measurements.
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.
A Rao-Blackwellized Particle Filter for Topological Mapping

(Georgia Institute of Technology, 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.
Data-Driven MCMC for Learning and Inference in Switching Linear Dynamic Systems

(Georgia Institute of Technology, 2005-07) Oh, Sang Min ; Rehg, James M. ; Balch, Tucker ; Dellaert, Frank

Switching Linear Dynamic System (SLDS) models are a popular technique for modeling complex nonlinear dynamic systems. An SLDS has significantly more descriptive power than an HMM, but inference in SLDS models is computationally intractable. This paper describes a novel inference algorithm for SLDS models based on the Data- Driven MCMC paradigm. We describe a new proposal distribution which substantially increases the convergence speed. Comparisons to standard deterministic approximation methods demonstrate the improved accuracy of our new approach. We apply our approach to the problem of learning an SLDS model of the bee dance. Honeybees communicate the location and distance to food sources through a dance that takes place within the hive. We learn SLDS model parameters from tracking data which is automatically extracted from video. We then demonstrate the ability to successfully segment novel bee dances into their constituent parts, effectively decoding the dance of the bees.
Mixture Trees for Modeling and Fast Conditional Sampling with Applications in Vision and Graphics

(Georgia Institute of Technology, 2005-06) Dellaert, Frank ; Kwatra, Vivek ; Oh, Sang Min

We introduce mixture trees, a tree-based data-structure for modeling joint probability densities using a greedy hierarchical density estimation scheme. We show that the mixture tree models data efficiently at multiple resolutions, and present fast conditional sampling as one of many possible applications. In particular, the development of this datastructure was spurred by a multi-target tracking application, where memory-based motion modeling calls for fast conditional sampling from large empirical densities. However, it is also suited to applications such as texture synthesis, where conditional densities play a central role. Results will be presented for both these applications.
A Multi-Camera Pose Tracker for Assisting the Visually Impaired

(Georgia Institute of Technology, 2005-06) Dellaert, Frank ; Tariq, Sarah

6DOF Pose tracking is useful in many contexts, e.g., in augmented reality (AR) applications. In particular, we seek to assist visually impaired persons by providing them with an auditory interface to their environment through sonification. For this purpose, accurate head tracking in mixed indoor/outdoor settings is the key enabling technology. Most of the work to date has concentrated on single-camera systems with a relatively small field of view, but this presents a fundamental limit on the accuracy of such systems. We present a multi-camera pose tracker that handles an arbitrary configuration of cameras rigidly fixed to the object of interest. By using multiple cameras, we increase both the robustness and the accuracy by which a 6-DOF pose is tracked. However, in a multi-camera rig setting, earlier methods for determining the unknown pose from three world-to-camera correspondences are no longer applicable, as they all assume a common center of projection. In this paper, we present a RANSAC-based method that copes with this limitation and handles multi-camera rigs. In addition, we present quantitative results to serve as a design guide for full system deployments based on multi-camera rigs. Our formulation is completely general, in that it handles an arbitrary, heterogeneous collection of cameras in any arbitrary configuration.
What Are the Ants Doing? Vision-Based Tracking and Reconstruction of Control Programs

(Georgia Institute of Technology, 2005-04) Balch, Tucker ; Dellaert, Frank ; Delmotte, Florent ; Khan, Zia ; Egerstedt, Magnus B.

In this paper, we study the problem of going from a real-world, multi-agent system to the generation of control programs in an automatic fashion. In particular, a computer vision system is presented, capable of simultaneously tracking multiple agents, such as social insects. Moreover, the data obtained from this system is fed into a mode-reconstruction module that generates low-complexity control programs, i.e. strings of symbolic descriptions of control-interrupt pairs, consistent with the empirical data. The result is a mechanism for going from the real system to an executable implementation that can be used for controlling multiple mobile robots.
A Markov Chain Monte Carlo Approach to Closing the Loop in SLAM

(Georgia Institute of Technology, 2005-04) Kaess, Michael ; Dellaert, Frank

The problem of simultaneous localization and mapping has received much attention over the last years. Especially large scale environments, where the robot trajectory loops back on itself, are a challenge. In this paper we introduce a new solution to this problem of closing the loop. Our algorithm is EM-based, but differs from previous work. The key is a probability distribution over partitions of feature tracks that is determined in the E-step, based on the current estimate of the motion. This virtual structure is then used in the M-step to obtain a better estimate for the motion. We demonstrate the success of our algorithm in experiments on real laser data.
Inference In The Space Of Topological Maps: An MCMC-based Approach

(Georgia Institute of Technology, 2004-09) Ranganathan, Ananth ; Dellaert, Frank

While probabilistic techniques have been considered extensively in the context of metric maps, no general purpose probabilistic 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 the available sensor measurements. The PTM is obtained through the use of MCMC-based Bayesian inference over the space of all possible topologies. It is shown that the space of all topologies is equivalent to the space of set partitions of all available measurements. While the space of possible topologies is intractably large, our use of Markov chain Monte Carlo sampling to infer the approximate histograms overcomes the combinatorial nature of this space and provides a general solution to the correspondence problem in the context of topological mapping. We present experimental results that validate our technique and generate good maps even when using only odometry as the sensor measurements.