| Abstract: |  In this work, we focus on the combination of visual and depth data in object detection. We propose an algorithm for object detection using a sliding window technique and a Bayesian-like classifier based on random ferns with Haar features. We test the algorithm on the car detection problem using a mobile robot for urban search and rescue equipped with a camera and a 3D lidar. The detector finds cars in a scene and estimates their approximate orientation. We show that the fusion of the visual images and the 3D data significantly improves the detection performance in comparison with utilizing only the visual data. The detector is able to overcome poor data quality from one of the sensors in harsh conditions.
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