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 13

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.
Semantic Modeling of Places using Objects

(Georgia Institute of Technology, 2007-06) Ranganathan, Ananth ; Dellaert, Frank

While robot mapping has seen massive strides recently, higher level abstractions in map representation are still not widespread. Maps containing semantic concepts such as objects and labels are essential for many tasks in manmade environments as well as for human-robot interaction and map communication. In keeping with this aim, we present a model for places using objects as the basic unit of representation. Our model is a 3D extension of the constellation object model, popular in computer vision, in which the objects are modeled by their appearance and shape. The 3D location of each object is maintained in a coordinate frame local to the place. The individual object models are learned in a supervised manner using roughly segmented and labeled training images. Stereo range data is used to compute 3D locations of the objects. We use the Swendsen-Wang algorithm, a cluster MCMC method, to solve the correspondence problem between image features and objects during inference. We provide a technique for building panoramic place models from multiple views of a location. An algorithm for place recognition by comparing models is also provided. Results are presented in the form of place models inferred in an indoor environment.We envision the use of our place model as a building block towards a complete object-based semantic mapping system.
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.
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.
Probabilistic Topological Mapping for Mobile Robots using Urn Models

(Georgia Institute of Technology, 2007) Ranganathan, Ananth ; Dellaert, Frank

We present an application of Bayesian modeling and inference to topological mapping in robotics. This is a potentially difficult problem due to (a) the combinatorial nature of the state space, and (b) perceptual aliasing by which two different landmarks in the environment can appear similar to the robot's sensors. Hence, this presents a challenging approximate inference problem, complicated by the fact that the form of the prior on topologies is far from obvious. We deal with the latter problem by introducing the use of urn models, which very naturally encode prior assumptions in the domain of topological mapping. Secondly, we advance simulated tempering as the basis of two rapidly mixing approximate inference algorithms, based on Markov chain Monte Carlo (MCMC) and Sequential Importance Sampling (SIS), respectively. These algorithms converge quickly even though the posterior being estimated is highly peaked and multimodal. Experiments on real robots and in simulation demonstrate the efficiency and robustness of our technique.
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.
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.
Bayesian Inference in the Space of Topological Maps

(Georgia Institute of Technology, 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.
Data Driven MCMC for Appearance-Based Topological Mapping

(Georgia Institute of Technology, 2005) Ranganathan, Ananth ; Dellaert, Frank

Probabilistic techniques have become the mainstay of robotic mapping, particularly for generating metric maps. In previous work, we have presented a hitherto nonexistent general purpose probabilistic framework for dealing with topological mapping. This involves the creation of Probabilistic Topological Maps (PTMs), a sample-based representation that approximates the posterior distribution over topologies given available sensor measurements. The PTM is inferred using Markov Chain Monte Carlo (MCMC) that overcomes the combinatorial nature of the problem. In this paper, we address the problem of integrating appearance measurements into the PTM framework. Specifically, we consider appearance measurements in the form of panoramic images obtained from a camera rig mounted on a robot. We also propose improvements to the efficiency of the MCMC algorithm through the use of an intelligent data-driven proposal distribution. We present experiments that illustrate the robustness and wide applicability of our algorithm.
A Variational inference method for Switching Linear Dynamic Systems

(Georgia Institute of Technology, 2005) Oh, Sang Min ; Ranganathan, Ananth ; Rehg, James M. ; Dellaert, Frank

This paper aims to present a structured variational inference algorithm for switching linear dynamical systems (SLDSs) which was initially introduced by Pavlovic and Rehg. Starting with the need for the variational approach, we proceed to the derivation of the generic (model-independent) variational update formulas which are obtained under the mean field assumption. This leads us to the derivation of an approximate variational inference algorithm for an SLDS. The details of deriving the SLDS-specific variational update equations are presented.