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279
A comparison of affine region detectors
- International Journal of Computer Vision
, 2005
"... The paper gives a snapshot of the state of the art in affine covariant region detectors, and compares their performance on a set of test images under varying imaging conditions. Six types of detectors are included: detectors based on affine normalization around Harris [24, 34] and Hessian points [24 ..."
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Cited by 364 (19 self)
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The paper gives a snapshot of the state of the art in affine covariant region detectors, and compares their performance on a set of test images under varying imaging conditions. Six types of detectors are included: detectors based on affine normalization around Harris [24, 34] and Hessian points [24], as proposed by Mikolajczyk and Schmid and by Schaffalitzky and Zisserman; a detector of ‘maximally stable extremal regions’, proposed by Matas et al. [21]; an edge-based region detector [45] and a detector based on intensity extrema [47], proposed by Tuytelaars and Van Gool; and a detector of ‘salient regions’, proposed by Kadir, Zisserman and Brady [12]. The performance is measured against changes in viewpoint, scale, illumination, defocus and image compression. The objective of this paper is also to establish a reference test set of images and performance software, so that future detectors can be evaluated in the same framework. 1
ORB: an efficient alternative to SIFT or SURF
- In ICCV
"... Feature matching is at the base of many computer vision problems, such as object recognition or structure from motion. Current methods rely on costly descriptors for detection and matching. In this paper, we propose a very fast binary descriptor based on BRIEF, called ORB, which is rotation invarian ..."
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Cited by 171 (0 self)
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Feature matching is at the base of many computer vision problems, such as object recognition or structure from motion. Current methods rely on costly descriptors for detection and matching. In this paper, we propose a very fast binary descriptor based on BRIEF, called ORB, which is rotation invariant and resistant to noise. We demonstrate through experiments how ORB is at two orders of magnitude faster than SIFT, while performing as well in many situations. The efficiency is tested on several real-world applications, including object detection and patch-tracking on a smart phone. 1.
Evaluation of features detectors and descriptors based on 3d objects
- IJCV
, 2005
"... We explore the performance of a number of popular feature detectors and descriptors in matching 3D object features across viewpoints and lighting conditions. To this end we design a method, based on intersecting epipolar constraints, for providing ground truth correspondence automatically. We collec ..."
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Cited by 139 (2 self)
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We explore the performance of a number of popular feature detectors and descriptors in matching 3D object features across viewpoints and lighting conditions. To this end we design a method, based on intersecting epipolar constraints, for providing ground truth correspondence automatically. We collect a database of 100 objects viewed from 144 calibrated viewpoints under three different lighting conditions. We find that the combination of Hessian-affine feature finder and SIFT features is most robust to viewpoint change. Harris-affine combined with SIFT and Hessianaffine combined with shape context descriptors were best respectively for lighting changes and scale changes. We also find that no detector-descriptor combination performs well with viewpoint changes of more than 25-30 ◦. 1
Two years of visual odometry on the Mars Exploration Rovers
- Journal of Field Robotics, Special Issue on Space Robotics
, 2007
"... NASA’s two Mars Exploration Rovers (MER) have successfully demonstrated a robotic Visual Odometry capability on another world for the first time. This provides each rover with accurate knowledge of its position, which allows it to autonomously detect and compensate for any unforeseen slip encountere ..."
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Cited by 98 (4 self)
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NASA’s two Mars Exploration Rovers (MER) have successfully demonstrated a robotic Visual Odometry capability on another world for the first time. This provides each rover with accurate knowledge of its position, which allows it to autonomously detect and compensate for any unforeseen slip encountered during a drive. It has enabled the rovers to drive safely and more effectively in highly-sloped and sandy terrains, and has resulted in increased mission science return by reducing the number of days required to drive into interesting areas. The MER Visual Odometry system comprises onboard software for comparing stereo pairs taken by the pointable mast-mounted 45 degree FOV Navigation cameras (NAVCAMs). The system computes an update to the 6 Degree Of Freedom rover pose (x, y, z, roll, pitch, yaw) by tracking the motion of autonomously-selected terrain features between two pairs of 256x256 stereo images. It has demonstrated good performance with high rates of successful convergence (97 % on Spirit, 95 % on Opportunity), successfully detected slip ratios as high as 125%, and measured changes as small as 2 mm, even while driving on slopes as high as 31 degrees. During the first two years of operations, Visual Odometry evolved from an “extra credit ” capability into a critical vehicle safety system. In this paper we describe our Visual Odometry algorithm, discuss several driving strategies that rely on it (including Slip Checks, Keep-out Zones, and Wheel Dragging), and summarize its results from the first two years of operations on Mars. 1
Cooperative Localization in Wireless Networks
"... Location-aware technologies will revolutionize many aspects of commercial, public service, and military sectors and are expected to spawn numerous unforeseen applications. A new era of highly accurate ubiquitous location-awareness is on the horizon, enabled by a paradigm of cooperation between node ..."
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Cited by 78 (22 self)
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Location-aware technologies will revolutionize many aspects of commercial, public service, and military sectors and are expected to spawn numerous unforeseen applications. A new era of highly accurate ubiquitous location-awareness is on the horizon, enabled by a paradigm of cooperation between nodes. In this paper, we give an overview of cooperative localization approaches and apply them to ultra-wide bandwidth (UWB) wireless networks. UWB transmission technology is particularly attractive for short- to medium-range localization, especially in GPS-denied environments; wide transmission bandwidths enable robust communication in dense multi-path scenarios, and the ability to resolve sub-nanosecond delays results in centimeterlevel distance resolution. We will describe several cooperative localization algorithms and quantify their performance, based on realistic UWB ranging models developed through an extensive measurement campaign using FCC-compliant UWB radios. We will also present a powerful localization algorithm by mapping a graphical model for statistical inference onto the network topology, which results in a net-factor graph, and by developing a suitable net-message passing schedule. The resulting algorithm (SPAWN) is fully distributed, can cope with a wide variety of scenarios, and requires little communication overhead to achieve accurate and robust localization.
SLAM — Loop Closing with Visually Salient Features
, 2005
"... Within the context of Simultaneous Localisation and Mapping (SLAM), “loop closing ” is the task of deciding whether or not a vehicle has, after an excursion of arbitrary length, returned to a previously visited area. Reliable loop closing is both essential and hard. It is without doubt one of the gr ..."
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Cited by 65 (7 self)
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Within the context of Simultaneous Localisation and Mapping (SLAM), “loop closing ” is the task of deciding whether or not a vehicle has, after an excursion of arbitrary length, returned to a previously visited area. Reliable loop closing is both essential and hard. It is without doubt one of the greatest impediments to long term, robust SLAM. This paper illustrates how visual features, used in conjunction with scanning laser data, can be used to a great advantage. We use the notion of visual saliency to focus the selection of suitable (affine invariant) image-feature descriptors for storage in a database. When queried with a recently taken image the database returns the capture time of matching images. This time information is used to discover loop closing events. Crucially this is achieved independently of estimated map and vehicle location. We integrate the above technique into a SLAM algorithm using delayed vehicle states and scan matching to form interpose geometric constraints. We present initial results using this system to close loops (around 100m) in an indoor environment.
Vision-based SLAM using the rao-blackwellised particle filter
- In IJCAI Workshop on Reasoning with Uncertainty in Robotics
, 2005
"... We consider the problem of Simultaneous Localization and Mapping (SLAM) from a Bayesian point of view using the Rao-Blackwellised Particle Filter (RBPF). We focus on the class of indoor mobile robots equipped with only a stereo vision sensor. Our goal is to construct dense metric maps of natural 3D ..."
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Cited by 61 (3 self)
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We consider the problem of Simultaneous Localization and Mapping (SLAM) from a Bayesian point of view using the Rao-Blackwellised Particle Filter (RBPF). We focus on the class of indoor mobile robots equipped with only a stereo vision sensor. Our goal is to construct dense metric maps of natural 3D point landmarks for large cyclic environments in the absence of accurate landmark position measurements and reliable motion estimates. Landmark estimates are derived from stereo vision and motion estimates are based on visual odometry. We distinguish between landmarks using the Scale Invariant Feature Transform (SIFT). Our work defers from current popular approaches that rely on reliable motion models derived from odometric hardware and accurate landmark measurements obtained with laser sensors. We present results that show that our model is a successful approach for vision-based SLAM, even in large environments. We validate our approach experimentally, producing the largest and most accurate vision-based map to date, while we identify the areas where future research should focus in order to further increase its accuracy and scalability to significantly larger environments. I.
R.: An image-based system for urban navigation
, 2004
"... We describe the prototype of a system intended to allow a user to navigate in an urban environment using a mobile telephone equipped with a camera. The system uses a database of views of building facades to determine the pose of a query view provided by the user. Our method is based on a novel wide- ..."
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Cited by 61 (0 self)
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We describe the prototype of a system intended to allow a user to navigate in an urban environment using a mobile telephone equipped with a camera. The system uses a database of views of building facades to determine the pose of a query view provided by the user. Our method is based on a novel wide-baseline matching algorithm that can identify corresponding building facades in two views despite significant changes of viewpoint and lighting. We show that our system is capable of localising query views reliably in a large part of Cambridge city centre. 1
Vision-based slam: Stereo and monocular approaches
- Int. J. Compt. Vision
, 2006
"... Building a spatially consistent model is a key functionality to endow a mobile robot with autonomy. Without an initial map or an absolute localization means, it requires to concurrently solve the localization and mapping problems. For this purpose, vision is a powerful sensor, because it provides da ..."
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Cited by 54 (2 self)
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Building a spatially consistent model is a key functionality to endow a mobile robot with autonomy. Without an initial map or an absolute localization means, it requires to concurrently solve the localization and mapping problems. For this purpose, vision is a powerful sensor, because it provides data from which stable features can be extracted and matched as the robot moves. But it does not directly provide 3D information, which is a difficulty for estimating the geometry of the environment. This article presents two approaches to the SLAM problem using vision: one with stereovision, and one with monocular images. Both approaches rely on a robust interest point matching algorithm that works in very diverse environments. The stereovision based approach is a classic SLAM implementation, whereas the monocular approach introduces a new way to initialize landmarks. Both approaches are analyzed and compared with extensive experimental results, with a rover and a blimp. 1
Monocular vision for mobile robot localization and autonomous navigation
- JOURNAL OF COMPUTER VISION
, 2007
"... This paper presents a new real-time localization system for a mobile robot. We show that autonomous navigation is possible in outdoor situation with the use of a single camera and natural landmarks. To do that, we use a three step approach. In a learning step, the robot is manually guided on a pat ..."
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Cited by 53 (0 self)
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This paper presents a new real-time localization system for a mobile robot. We show that autonomous navigation is possible in outdoor situation with the use of a single camera and natural landmarks. To do that, we use a three step approach. In a learning step, the robot is manually guided on a path and a video sequence is recorded with a front looking camera. Then a structure from motion algorithm is used to build a 3D map from this learning sequence. Finally in the navigation step, the robot uses this map to compute its localization in real-time and it follows the learning path or a slightly different path if desired. The vision algorithms used for map building and localization are first detailed. Then a large part of the paper is dedicated to the experimental evaluation of the accuracy and robustness of our algorithms based on experimental data collected during two years in various environments.
