Odometry-Based Structure from Motion

Woock, Philipp; Pagel, Frank; Grinberg, Michael; Willersinn, Dieter

doi:10.1109/ivs.2007.4290266

Cited by 8 publications

(4 citation statements)

References 5 publications

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“…The motion is defined with respect to the COG, because many sensors are calibrated in reference to the COG. Similar to [14], we use the velocity and the yaw rate of the vehicle to estimate the trajectory of the vehicle. The velocity v and the yaw rateφ of the vehicle are assumed to be constant for the short time interval T .…”

Section: Extrinsic Parametersmentioning

confidence: 99%

Motion compensation for obstacle detection based on homography and odometric data with virtual camera perspectives

Miksch

Yang

Zimmermann³

2010

2010 IEEE Intelligent Vehicles Symposium

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In this paper we present a method to compensate the image motion of a monocular camera on a moving vehicle in order to detect obstacles. Due to the camera motion, the road surface induces a characteristic image motion between two camera shots. The motion of the camera is determined by the use of odometric data received from the CAN-bus, and the position and orientation of the road is continuously estimated with camera self-calibration. This all leads to a motion field which is predicted based on homography. To prevent the drawbacks of the real camera perspective, different virtual camera perspectives are presented in combination with motion compensation. Possible virtual perspectives are the bird's eye view and image rectification. In addition, a non-linear camera model is used which does not limit the range of obstacle detection to a certain distance and efficiently uses the available image information.

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Section: Extrinsic Parametersmentioning

confidence: 99%

Motion compensation for obstacle detection based on homography and odometric data with virtual camera perspectives

Miksch

Yang

Zimmermann³

2010

2010 IEEE Intelligent Vehicles Symposium

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“…Ego-motion estimation and compensation are done using vehicle odometry data that were shown to deliver sufficient accuracy even for the structure-from-motion task. 21…”

Section: Overall System Descriptionmentioning

confidence: 99%

Handling of split-and-merge effects and occlusions using feature-based probabilistic data association

Grinberg

Ohr

2010

Intelligent Robots and Computer Vision XXVII: Algorithms and Techniques

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One of the big challenges in multi-target tracking is the track management and correct data association between measurements and tracks. Major reason for tracking errors are detection failures such as merged, split, incomplete or missed detections as well as clutter-based detections (phantom objects). Those effects combined with uncertainties in existence and number of objects in the scene as well as uncertainties in their observability and dynamic object state lead to gross tracking errors. In this contribution we present an algorithm for visual detection and tracking of multiple extended targets which is capable of coping with occlusions and split and merge effects. Unlike most of the state-of-the-art approaches we utilize information about the measurements' composition gained through tracking dedicated feature points in the image and in 3D space, which allows us to reconstruct the desired object characteristics from the data even in the case of detection errors due to above-mentioned reasons. The proposed Feature-Based Probabilistic Data Association approach resolves data association ambiguities in a soft threshold-free decision based not only on target state prediction but also on the existence and observability estimation modeled as two additional Markov chains. A novel measurement reconstruction scheme allows for a correct innovation in case of split, merged and incomplete measurements realizing thus a detection-by-tracking approach. This process is assisted by a grid based object representation which offers a lower abstraction level of targets extent and is used for detailed occlusion analysis

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“…The sensor system under consideration here is a side-looking stereo video camera [19]. Ego-motion estimation and compensation is done using vehicle odometry data which was shown to deliver sufficient accuracy even for the structurefrom-motion task [20].…”

Section: Overall System Descriptionmentioning

confidence: 99%

Feature-Based Probabilistic Data Association (FBPDA) for visual multi-target detection and tracking under occlusions and split and merge effects

Grinberg

Ohr

Beyerer

2009

2009 12th International IEEE Conference on Intelligent Transportation Systems

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Uncertainties in the sensor data such as measurement noise, false detections caused by clutter, as well as merged, split, incomplete or missed detections due to a sensor malfunction or occlusions (both due to the limited sensor field of view and objects in the scene) make multi-target tracking a very complicated task. Thus one of the big challenges is track management and correct data association between detections and tracks. In this contribution we present an algorithm for visual detection and tracking of multiple extended targets under occlusions and split and merge effects. Unlike most of the state-of-the-art approaches we utilize low-level information integrating it in a unified approach based on a thresholdfree probabilistic conception. The introduced scheme makes it possible to utilize information about composition of the measurements gained through tracking of dedicated feature points in the image and resolves data association ambiguities in a soft decision using a globally optimal probabilistic data association approach. Beside existence evolution consideration we also exploit the spatial and temporal relationship between stably tracked points and tracked objects, which along with observability analysis, allows us for reconstruction of compatible measurements and thus correct track update even in cases of splits, merges and partial occlusions of the tracked targets.

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Odometry-Based Structure from Motion

Cited by 8 publications

References 5 publications

Motion compensation for obstacle detection based on homography and odometric data with virtual camera perspectives

Motion compensation for obstacle detection based on homography and odometric data with virtual camera perspectives

Handling of split-and-merge effects and occlusions using feature-based probabilistic data association

Feature-Based Probabilistic Data Association (FBPDA) for visual multi-target detection and tracking under occlusions and split and merge effects

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