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This paper examines the problem of estimating vehicle position and direction, i.e., pose, from a single vehicle-mounted camera. A drawback of pose estimation using vision only is that it fails when image information is poor. Consequently, other information sources, e.g., motion models and sensors, may be used to complement vision to improve the estimates. We propose to combine standard in-vehicle sensor data and vehicle motion models with the accuracy of local visual bundle adjustment. This means that pose estimates are optimized with regard not only to observed image features but also to a single-track vehicle model and standard in-vehicle sensors. The described method has been experimentally tested on challenging data sets at both low and high vehicle speeds as well as a data set with moving objects. The vehicle motion model in combination with in-vehicle sensors exhibit good accuracy in estimating planar vehicle motion. Results show that this property is preserved, when combining these information sources with vision. Furthermore, the accuracy obtained from vision-only in direction estimation is improved, primarily in situations in which there are few matched visual features.

This chapter describes a framework which uses augmentation techniques for performance evaluation of mobile computer vision systems. Computer vision systems use primarily image data to interpret the surrounding world, e.g. to detect, classify and track objects. The performance of mobile computer vision systems acting in unknown environments is inherently difficult to evaluate since, often, obtaining ground truth data is problematic. The proposed novel framework exploits the possibility to add new agents into a real data sequence collected in an unknown environment, thus making it possible to efficiently create augmented data sequences, including ground truth, to be used for performance evaluation. Varying the content in the data sequence by adding different agents or changing the behavior of an agent is straightforward, making the proposed framework very flexible. A key driver for using augmentation techniques to address computer vision performance is that the vision system output may be sensitive to the background data content. The method has been implemented and tested on a pedestrian detection system used for automotive collision avoidance. Results show that the method has potential to replace and complement physical testing, for instance by creating collision scenarios, which are difficult to test in reality, in particular in a real traffic environment.J. Nilsson ( ) Vehicle