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David G. Lowe

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14 papers

2 author rows

ICRA Conference 2011 Conference Paper

REIN - A fast, robust, scalable REcognition INfrastructure

Marius Muja
Radu Bogdan Rusu
Gary R. Bradski
David G. Lowe

A robust robot perception system intended to enable object manipulation needs to be able to accurately identify objects and their pose at high speeds. Since objects vary considerably in surface properties, rigidity and articulation, no single detector or object estimation method has been shown to provide reliable detection across object types to date. This indicates the need for an architecture that is able to quickly swap detectors, pose estimators, and filters, or to run them in parallel or serial and combine their results, preferably without any code modifications at all. In this paper, we present our implementation of such an infrastructure, ReIn (REcognition INfrastructure), to answer these needs. ReIn is able to combine a multitude of 2D/3D object recognition and pose estimation techniques in parallel as dynamically loadable plugins. It also provides an extremely efficient data passing architecture, and offers the possibility to change the parameters and initial settings of these techniques during their execution. In the course of this work we introduce two new classifiers designed for robot perception needs: BiGGPy (Binarized Gradient Grid Pyramids) for scalable 2D classification and VFH (Viewpoint Feature Histograms) for 3D classification and pose. We then show how these two classifiers can be easily combined using ReIn to solve object recognition and pose identification problems.

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ICRA Conference 2010 Conference Paper

Using stereo for object recognition

Scott Helmer
David G. Lowe

There has been significant progress recently in object recognition research, but many of the current approaches still fail for object classes with few distinctive features, and in settings with significant clutter and viewpoint variance. One such setting is visual search in mobile robotics, where tasks such as finding a mug or stapler require robust recognition. The focus of this paper is on integrating stereo vision with appearance based recognition to increase accuracy and efficiency. We propose a model that utilizes a chamfer-type silhouette classifier which is weighted by a prior on scale, which is robust to missing stereo depth information. Our approach is validated on a set of challenging indoor scenes containing mugs and shoes, where we find that priors remove a significant number of false positives, improving the average precision by 0. 2 on each dataset. We additionally experiment with an additional classifer by Felzenszwalb et al. to demonstrate the approach's robustness.

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ICRA Conference 2008 Conference Paper

Informed visual search: Combining attention and object recognition

Per-Erik Forssén
David Meger
Kevin Lai
Scott Helmer
James J. Little
David G. Lowe

This paper studies the sequential object recognition problem faced by a mobile robot searching for specific objects within a cluttered environment. In contrast to current state-of-the-art object recognition solutions which are evaluated on databases of static images, the system described in this paper employs an active strategy based on identifying potential objects using an attention mechanism and planning to obtain images of these objects from numerous viewpoints. We demonstrate the use of a bag-of-features technique for ranking potential objects, and show that this measure outperforms geometric matching for invariance across viewpoints. Our system implements informed visual search by prioritising map locations and re-examining promising locations first. Experimental results demonstrate that our system is a highly competent object recognition system that is capable of locating numerous challenging objects amongst distractors.

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ICRA Conference 2003 Conference Paper

3D localization and tracking in unknown environments

Parvaneh Saeedi
David G. Lowe
Peter D. Lawrence

This paper describes a vision-based system for 3D localization and tracking of a mobile robot in an unmodified environment. The system includes a mountable head with three on-board stereo CCD cameras that can be installed on the robot. There the main emphasis is on the ability to estimate the geometric information of the robot independently from any prior scene knowledge, landmark or extra sensory device. Distinctive scene features are identified using a novel algorithm and their 3D locations are estimated with a stereo algorithm. Using multi-stage feature tracking and motion estimation in a symbolic manner, precise motion vectors are obtained. The 3D positions of the scene features are updated by a Kalman filtering process. Experimental results show that robust tracking and localization can be achieved using our vision system.

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IROS Conference 2002 Conference Paper

Global localization using distinctive visual features

Stephen Se
David G. Lowe
James J. Little

We have previously developed a mobile robot system which uses scale invariant visual landmarks to localize and simultaneously build a 3D map of the environment In this paper, we look at global localization, also known as the kidnapped robot problem, where the robot localizes itself globally, without any prior location estimate. This is achieved by matching distinctive landmarks in the current frame to a database map. A Hough transform approach and a random sample consensus (RANSAC) approach for global localization are compared, showing that RANSAC is much more efficient. Moreover, robust global localization can be achieved by matching a small sub-map of the local region built from multiple frames.

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IROS Conference 2002 Conference Paper

Vision-based mapping with backward correction

Stephen Se
David G. Lowe
James J. Little

We consider the problem of creating a consistent alignment of multiple 3D submaps containing distinctive visual landmarks in an unmodified environment. An efficient map alignment algorithm based on landmark specificity is proposed to align submaps. This is followed by a global minimization using the close-the-loop constraint. Landmark uncertainty is taken into account in the pairwise alignment and the global minimization process. Experiments show that the pairwise alignment of submaps with backward correction produces a consistent global 3D map. Our vision-based mapping approach using sparse 3D data is different from other existing approaches which use dense 2D range data from laser or sonar rangefinders.

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IROS Conference 2001 Conference Paper

Local and global localization for mobile robots using visual landmarks

Stephen Se
David G. Lowe
James J. Little

Our mobile robot system uses scale-invariant visual landmarks to localize itself and build a 3D map of the environment simultaneously. As image features are not noise-free, we carry out error analysis and use Kalman filters to track the 3D landmarks, resulting in a database map with landmark positional uncertainty. By matching a set of landmarks as a whole, our robot can localize itself globally based on the database containing landmarks of sufficient distinctiveness. Experiments show that recognition of position within a map without any prior estimate can be achieved using the scale-invariant landmarks.

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ICRA Conference 2001 Conference Paper

Vision-based Mobile Robot Localization And Mapping using Scale-Invariant Features

Stephen Se
David G. Lowe
James J. Little

A key component of a mobile robot system is the ability to localize itself accurately and build a map of the environment simultaneously. In this paper, a vision-based mobile robot localization and mapping algorithm is described which uses scale-invariant image features as landmarks in unmodified dynamic environments. These 3D landmarks are localized and robot ego-motion is estimated by matching them, taking into account the feature viewpoint variation. With our Triclops stereo vision system, experiments show that these features are robustly matched between views, 3D landmarks are tracked, robot pose is estimated and a 3D map is built.

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ICRA Conference 2000 Conference Paper

3D Motion Tracking of a Mobile Robot in a Natural Environment

Parvaneh Saeedi
Peter D. Lawrence
David G. Lowe

This paper presents a vision-based tracking system suitable for autonomous robot vehicle guidance. The system includes a head with three on-board CCD cameras, which can be mounted anywhere on a mobile vehicle. By processing consecutive trinocular sets of precisely aligned and rectified images, the local 3D trajectory of the vehicle in an unstructured environment can be tracked. First, a 3D representation of stable features in the image scene is generated using a stereo algorithm. Next, motion is estimated by trading matched features over time. The motion equation with 6-DOF is then solved using an iterative least squares fit algorithm. Finally, a Kalman filter implementation is used to optimize the world representation of scene features.

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ICRA Conference 1997 Conference Paper

Model-based telerobotics with vision

John E. Lloyd
Jeffrey S. Beis
Dinesh K. Pai
David G. Lowe

We describe an implemented model-based telerobotic system designed to investigate assembly and other tasks involving contact and manipulation of known objects. Key features of our system include ease of maintaining a world model at the operator site and a task-centric operator interface. Our system incorporates gray-scale model-based vision to assist in building and maintaining the local model. The local model is used to provide a task-centric operator interface, emphasizing the natural and direct manipulation of objects, with the robot's presence indicated in a more abstract fashion. The operator interface is designed to work with widely available and inexpensive desktop computers with low DOF input devices (such as a mouse). We also describe experimental results to date, which include performing assembly-like tasks over the Internet.

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ICRA Conference 1997 Conference Paper

Temporally coherent stereo: improving performance through knowledge of motion

Vladimir Tucakov
David G. Lowe

This paper introduces the idea of temporally extending the results of a stereo algorithm in order to improve the algorithm's performance. This approach anticipates the changes between two consecutive depth maps resulting from the motion of the cameras. Uncertainties in motion are accounted for by computation of an ambiguity area and a resulting disparity range for each pixel. The computation is used to verify and refine the anticipated values, rather than calculate them without prior knowledge. The paper compares the performance of the algorithm under different constraints on motion. Speedups of up to 400% are achieved without significant errors.

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AIJ Journal 1987 Journal Article

Three-dimensional object recognition from single two-dimensional images

David G. Lowe

A computer vision system has been implemented that can recognize three-dimensional objects from unknown viewpoints in single gray-scale images. Unlike most other approaches, the recognition is accomplished without any attempt to reconstruct depth information bottom-up from the visual input. Instead, three other mechanisms are used that can bridge the gap between the two-dimensional image and knowledge of three-dimensional objects. First, a process of perceptual organization is used to form groupings and structures in the image that are likely to be invariant over a wide range of viewpoints. Second, a probabilistic ranking method is used to reduce the size of the search space during model-based matching. Finally, a process of spatial correspondence brings the projections of three-dimensional models into direct correspondence with the image by solving for unknown viewpoint and model parameters. A high level of robustness in the presence of occlusion and missing data can be achieved through full application of a viewpoint consistency constraint. It is argued that similar mechanisms and constraints form the basis for recognition in human vision.

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IJCAI Conference 1985 Conference Paper

Visual Recognition from Spatial Correspondence and Perceptual Organization

David G. Lowe

Depth reconstruction from the two-dimensional image plays an important role in certain visual tasks and has been a major focus of of computer vision research. However, in this paper we argue that most instances of recognition in human and machine vision can best be performed without the preliminary reconstruction of depth. Three other mechanisms are described that can be used to bridge the gap between the two-dimensional image and knowledge of three-dimensional objects. First, a process of perceptual organization can be used to form groupings and structures in the image that are likely to be invariant over a wide range of viewpoints. Secondly, evidential reasoning can be used to combine evidence from these groupings and other sources of information to reduce the size of the search-space during model-based matching. Finally, a process of spatial correspondence can be used to bring the projections of three-dimensional models into direct correspondence with the image by solving for unknown viewpoint and model parameters. These methods have been combined in an experimental computer vision system named SCERPO. This system has demonstrated the use of these methods for the recognition of objects from unknown viewpoints in single gray-scale images.

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AAAI Conference 1983 Conference Paper

Perceptual Organization as a Basis for Visual Recognition

David G. Lowe

Evidence is prescnled showiug that bottom-up grouping of im&ge features is usually prerequisite to the recognition and in terprc tation of images. WC describe three functions of Ihcse groupings: 1) scgmcnlation, 2) three-dimcnsiomrl intcrpreta, tion, and 3) stable descriptions for accessjug object models. Scvernl principlcc J are hypothesized for dctermining which image relations should be formed: relations are significant to the extent that they are unlikely to have arisen by accident from the surrounding distribution of features, relations can only be formed where there are few altcrnatives within tilt same proximity, and relations must be based on properties which are invariant over a range of imaging conditions. Using these principles we develop an algorithm for curve segmentation which detects significant structure at multiple rcsofutions, including the linking of segments on the basis of curvilinearity. The algorithm is a. ble to detect structures \vhich no single-resolution algorithm could detect. Its j>crformance is demonstrated OR synthetic and natural image data.

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