Robust visual odometry for vehicle localization in urban environments

Parra, Isao; Sotelo, Miguel Ángel; Llorca, David Fernández; Ocaña, Manuel

doi:10.1017/s026357470900575x

Cited by 39 publications

(36 citation statements)

References 13 publications

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“…For this purpose a RANSAC based on non-linear leastsquares method was developed for a previous visual odometry system. A complete description of this method can be found in [11] and [12].…”

Section: Visual Odometry Using Weighted Non-linear Estimationmentioning

confidence: 99%

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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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“…For this purpose a RANSAC based on non-linear leastsquares method was developed for a previous visual odometry system. A complete description of this method can be found in [11] and [12].…”

Section: Visual Odometry Using Weighted Non-linear Estimationmentioning

confidence: 99%

“…In our previous work [11] the ego-motion of the vehicle relative to the road is computed using a stereo-vision system mounted next to the rear view mirror of the car. Feature points are matched between pairs of frames and linked into 3D trajectories.…”

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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“…[8][9][10][11] One of the most popular approaches is visual odometry, which is defined as the incremental online estimation of robot motion from image sequences using an on-board camera. available at http://www.ual.es/personal/rgonzalez/videosVO.…”

Section: Introductionmentioning

confidence: 99%

Combined visual odometry and visual compass for off-road mobile robots localization

et al. 2011

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SUMMARYIn this paper, we present the work related to the application of a visual odometry approach to estimate the location of mobile robots operating in off-road conditions. The visual odometry approach is based on template matching, which deals with estimating the robot displacement through a matching process between two consecutive images. Standard visual odometry has been improved using visual compass method for orientation estimation. For this purpose, two consumer-grade monocular cameras have been employed. One camera is pointing at the ground under the robot, and the other is looking at the surrounding environment. Comparisons with popular localization approaches, through physical experiments in off-road conditions, have shown the satisfactory behavior of the proposed strategy.

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“…The presented method is based on a stereo vision system [12]. The visual odometry estimation is normally performed by means of detecting and tracking feature points between consecutive frames [14]. This visual odometry estimation makes use of the dense disparity map [17] to detect the road in front of the vehicle.…”

Section: Introductionmentioning

confidence: 99%

Estimation and Prediction of the Vehicle’s Motion Based on Visual Odometry and Kalman Filter

Musleh

Martín

Escalera

et al. 2012

Advanced Concepts for Intelligent Vision Systems

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Abstract. The movement of the vehicle is an useful information for different applications, such as driver assistant systems or autonomous vehicles. This information can be known by different methods, for instance, by using a GPS or by means of the visual odometry. However, there are some situations where both methods do not work correctly. For example, there are areas in urban environments where the signal of the GPS is not available, as tunnels or streets with high buildings. On the other hand, the algorithms of computer vision are affected by outdoor environments, and the main source of difficulties is the variation in the ligthing conditions. A method to estimate and predict the movement of the vehicle based on visual odometry and Kalman filter is explained in this paper. The Kalman filter allows both filtering and prediction of vehicle motion, using the results from the visual odometry estimation.

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Robust visual odometry for vehicle localization in urban environments

Abstract: doi:10.1017/S026357470900575

Cited by 39 publications

References 13 publications

Visual odometry and map fusion for GPS navigation assistance

Visual odometry and map fusion for GPS navigation assistance

Combined visual odometry and visual compass for off-road mobile robots localization

Estimation and Prediction of the Vehicle’s Motion Based on Visual Odometry and Kalman Filter

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