Abstract
Several online real-world stereo datasets exist for the development and testing of algorithms in the fields of perception and navigation of autonomous vehicles. However, none of them was recorded in developing countries, and therefore they lack the particular challenges that can be found on their streets and roads, like abundant potholes, irregular speed bumpers, and peculiar flows of pedestrians. We introduce a novel dataset that possesses such characteristics. The stereo dataset was recorded in Mexico from a moving vehicle. It contains high-resolution stereo images which are complemented with direction and acceleration data obtained from an IMU, GPS data, and data from the car computer. This paper describes the structure and contents of our dataset files and presents reconstruction experiments that we performed on the data.
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Guzmán, R., Hayet, JB., Klette, R. (2015). Towards Ubiquitous Autonomous Driving: The CCSAD Dataset. In: Azzopardi, G., Petkov, N. (eds) Computer Analysis of Images and Patterns. CAIP 2015. Lecture Notes in Computer Science(), vol 9256. Springer, Cham. https://doi.org/10.1007/978-3-319-23192-1_49
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DOI: https://doi.org/10.1007/978-3-319-23192-1_49
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