Vision-Based SLAM: Stereo and Monocular Approaches

Lemaire, Thomas; Berger, Cyrille; Jung, Inhwa; Lacroix, Simon

doi:10.1007/s11263-007-0042-3

Cited by 218 publications

(107 citation statements)

References 41 publications

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“…In theory, using stereo cameras [18,39] solves the problem of requiring the camera to travel, since the baseline required to triangulate features is built-in. In practice, however, using stereo cameras is only a partial remedy, since the baseline has to be significant in relation to the distance to the environment in order to reliably estimate depth.…”

Section: Further Related Workmentioning

confidence: 99%

Live tracking and mapping from both general and rotation-only camera motion

Gauglitz

Sweeney

Ventura

et al. 2012

2012 IEEE International Symposium on Mixed and Augmented Reality (ISMAR)

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We present an approach to real-time tracking and mapping that supports any type of camera motion in 3D environments, that is, general (parallax-inducing) as well as rotation-only (degenerate) motions. Our approach effectively generalizes both a panorama mapping and tracking system and a keyframe-based Simultaneous Localization and Mapping (SLAM) system, behaving like one or the other depending on the camera movement. It seamlessly switches between the two and is thus able to track and map through arbitrary sequences of general and rotation-only camera movements.Key elements of our approach are to design each system component such that it is compatible with both panoramic data and Structure-from-Motion data, and the use of the 'Geometric Robust Information Criterion' to decide whether the transformation between a given pair of frames can best be modeled with an essential matrix E, or with a homography H. Further key features are that no separate initialization step is needed, that the reconstruction is unbiased, and that the system continues to collect and map data after tracking failure, thus creating separate tracks which are later merged if they overlap. The latter is in contrast to most existing tracking and mapping systems, which suspend tracking and mapping, thus discarding valuable data, while trying to relocalize the camera with respect to the initial map.We tested our system on a variety of video sequences, successfully tracking through different camera motions and fully automatically building panoramas as well as 3D structures.

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Section: Further Related Workmentioning

confidence: 99%

Live tracking and mapping from both general and rotation-only camera motion

Gauglitz

Sweeney

Ventura

et al. 2012

2012 IEEE International Symposium on Mixed and Augmented Reality (ISMAR)

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“…Ego-motion is provided by either visual odometry [1,15], SLAM [21,22,23] (self localization and mapping), or the inertial motion sensors of the vehicle. Stixel motion is obtained by computing optical flow correspondences.…”

Section: Figmentioning

confidence: 99%

An Evaluation Framework for Stereo-Based Driver Assistance

Schneider

Gehrig

Pfeiffer

et al. 2012

Lecture Notes in Computer Science

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Abstract. The accuracy of stereo algorithms or optical flow methods is commonly assessed by comparing the results against the Middlebury database. However, equivalent data for automotive or robotics applications rarely exist as they are difficult to obtain. As our main contribution, we introduce an evaluation framework tailored for stereo-based driver assistance able to deliver excellent performance measures while circumventing manual label effort. Within this framework one can combine several ways of ground-truthing, different comparison metrics, and use large image databases. Using our framework we show examples on several types of groundtruthing techniques: implicit ground truthing (e.g. sequence recorded without a crash occurred), robotic vehicles with high precision sensors, and to a small extent, manual labeling. To show the effectiveness of our evaluation framework we compare three different stereo algorithms on pixel and object level. In more detail we evaluate an intermediate representation called the Stixel World. Besides evaluating the accuracy of the Stixels, we investigate the completeness (equivalent to the detection rate) of the Stixel World vs. the number of phantom Stixels. Among many findings, using this framework enables us to reduce the number of phantom Stixels by a factor of three compared to the base parametrization. This base parametrization has already been optimized by test driving vehicles for distances exceeding 10000 km.

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“…Though using vision-based information "all the time" was initially explored (e.g., using video frame-to-frame relative rotation/translation information [4], or using more sophisticated vision SLAM approaches [5], [6]), this path was not pursued further due to concerns about both processing power requirements and overall robustness. The vision algorithms currently in the system have instead been implemented as a module that may or may not provide measurements, depending on the circumstances.…”

Section: Introductionmentioning

confidence: 99%

Development of vision-aided navigation for a wearable outdoor augmented reality system

Menozzi

Clipp

Wenger

et al. 2014

2014 IEEE/ION Position, Location and Navigation Symposium - PLANS 2014

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Abstract-This paper describes the development of visionaided navigation (i.e., pose estimation) for a wearable augmented reality system operating in natural outdoor environments. This system combines a novel pose estimation capability, a helmetmounted see-through display, and a wearable processing unit to accurately overlay geo-registered graphics on the user's view of reality. Accurate pose estimation is achieved through integration of inertial, magnetic, GPS, terrain elevation data, and computervision inputs. Specifically, a helmet-mounted forward-looking camera and custom computer vision algorithms are used to provide measurements of absolute orientation (i.e., orientation of the helmet with respect to the Earth). These orientation measurements, which leverage mountainous terrain horizon geometry and/or known landmarks, enable the system to achieve significant improvements in accuracy compared to GPS/INS solutions of similar size, weight, and power, and to operate robustly in the presence of magnetic disturbances. Recent field testing activities, across a variety of environments where these vision-based signals of opportunity are available, indicate that high accuracy (less than 10 mrad) in graphics geo-registration can be achieved. This paper presents the pose estimation process, the methods behind the generation of vision-based measurements, and representative experimental results.

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Vision-Based SLAM: Stereo and Monocular Approaches

Cited by 218 publications

References 41 publications

Live tracking and mapping from both general and rotation-only camera motion

Live tracking and mapping from both general and rotation-only camera motion

An Evaluation Framework for Stereo-Based Driver Assistance

Development of vision-aided navigation for a wearable outdoor augmented reality system

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