Real-time Localization in Outdoor Environments using Stereo Vision and Inexpensive GPS

Agrawal, Motilal; Konolige, Kurt

doi:10.1109/icpr.2006.962

Cited by 217 publications

(144 citation statements)

References 24 publications

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“…This measurement function is no longer linear in the frame variables, but it is easy to see that when 0 p is the origin, it reduces to (9). More importantly, (10) produces exactly the same PDF for 1 p as does SBA, when both use the same (arbitrary) fixed value for 0 p (see Appendix I for a proof). It is worth emphasizing the import of going from the large set of projective measurements ) , (…”

Section: Frame-frame Matchingmentioning

confidence: 98%

“…Fix p 0 to be the origin, and calculate the estimated value 1 p and its inverse covariance 11 H ) (from (8)) using SBA. These two values summarize the complicated nonlinear relationship between p 0 and p 1 (constructed from the feature measurements) as a Gaussian PDF.…”

Section: Frame-frame Matchingmentioning

confidence: 99%

“…In visual odometry, the pose is estimated by matching image features across several consecutive frames [1][18] [19]. Current techniques achieve very precise results, but pose errors grow unbounded with time, even when the camera stays in the same area, because there is no matching of frames that are close in space, but not time.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Frame-Frame Matching for Realtime Consistent Visual Mapping

Konolige¹,

Agrawal²

2007

Proceedings 2007 IEEE International Conference on Robotics and Automation

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Abstract. Many successful indoor mapping techniques employ frame-to-frame matching of laser scans to produce detailed local maps, as well as closing large loops. In this paper, we propose a framework for applying the same techniques to visual imagery, matching visual frames with large numbers of point features.The relationship between frames is kept as a nonlinear measurement, and can be used to solve large loop closures quickly. Both monocular (bearing-only) and binocular vision can be used to generate matches. Other advantages of our system are that no special landmark initialization is required, and large loops can be solved very quickly.

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Section: Frame-frame Matchingmentioning

confidence: 98%

Section: Frame-frame Matchingmentioning

confidence: 99%

See 1 more Smart Citation

Frame-Frame Matching for Realtime Consistent Visual Mapping

Konolige¹,

Agrawal²

2007

Proceedings 2007 IEEE International Conference on Robotics and Automation

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“…We use stereo visual odometry to estimate the frame-toframe motion of the robot following the basic approaches outlined in [16], [21], [22]. Although the robot undergoes some small pitching and rolling on our testing terrain, our initial experiments have thus far been limited to recovering a planar motion consisting of a rotation and forward translation (∆θ, ∆Z).…”

Section: Motion Estimationmentioning

confidence: 99%

Trail following with omnidirectional vision

Rasmussen

Kocamaz

2010

2010 IEEE/RSJ International Conference on Intelligent Robots and Systems

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Abstract-We describe a system which follows "trails" for autonomous outdoor robot navigation. Through a combination of visual cues provided by stereo omnidirectional color cameras and ladar-based structural information, the algorithm is able to detect and track rough paths despite widely varying tread material, border vegetation, and illumination conditions. The approaching trail region is simply modeled as a circular arc of constant width. Using an adaptive measure of color and brightness contrast between a hypothetical region and flanking areas, the tracker performs a robust randomized search for the most likely trail region and robot pose relative to it with no a priori appearance model. Stereo visual odometry improves tracker dynamics on uneven terrain and permits local obstacle map maintenance. A motion planner is also described which takes the trail shape estimate and local map to plan smooth trajectories around in-trail and near-trail hazards. Our system's performance is analyzed on several long sequences with diverse appearance and structural characteristics using ground-truth segmentations.

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“…Both monocular and stereo-based versions of visual odometry were developed in [2], although the monocular version needs additional improvements to run in real time, and the stereo version is limited to a frame rate of 13 images per second. In [5] a stereo system composed of two wide Field of View cameras was installed on a mobile robot together with a GPS receiver and classical encoders. The system was tested in outdoor scenarios on different runs under 150 m. with a precision in the estimation of the travelled distance of 2-5%.…”

Section: Introductionmentioning

confidence: 99%

Visual odometry and map fusion for GPS navigation assistance

Parra

Sotelo

Llorca

et al. 2011

2011 IEEE International Symposium on Industrial Electronics

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Abstract-This paper describes a new approach for improving the estimation of the global position of a vehicle in complex urban environments by means of visual odometry and map fusion. The visual odometry system is based on the compensation of the heterodasticity in the 3D input data using a weighted nonlinear least squares based system. RANdom SAmple Consensus (RANSAC) based on Mahalanobis distance is used for outlier removal. The motion trajectory information is used to keep track of the vehicle position in a digital map during GPS outages. The final goal is the autonomous vehicle outdoor navigation in large-scale environments and the improvement of current vehicle navigation systems based only on standard GPS. This research is oriented to the development of traffic collective systems aiming vehicle-infrastructure cooperation to improve dynamic traffic management. We provide examples of estimated vehicle trajectories and map fusion using the proposed method and discuss the key issues for further improvement.

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Real-time Localization in Outdoor Environments using Stereo Vision and Inexpensive GPS

Abstract: We describe a real-time, low-cost

Cited by 217 publications

References 24 publications

Frame-Frame Matching for Realtime Consistent Visual Mapping

Frame-Frame Matching for Realtime Consistent Visual Mapping

Trail following with omnidirectional vision

Visual odometry and map fusion for GPS navigation assistance

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