![]() ![]() This dataset is outdoor, so it fulfills one of our main requisites. The KITTI dataset 5 provides the RGB (stereo pair) and depth maps of 400 different layouts having a total of 1.6 k frames of roads from the city of Karlsruhe (Germany). The second problem is that the dataset is centered on the vision of a car driving in the street, while, in our case, we need the point of view of the pedestrians. The first is that it is a synthetic dataset, meaning that the frames are not photorealistic, with the subsequent problem of testing the system in real conditions. There are two main problems with this dataset. This is done by using the features that were tracked in the previous step and by rejecting outlier feature matches.SYNTHIA 2 or The SYNTHetic collection of Imagery and Annotations consists of a collection of photo-realistic frames rendered from a virtual city. Finally, an algorithm such as RANSAC is used for every stereo pair to incrementally estimate the camera pose. ![]() There is also an extra step of feature matching, but this time between two successive frames in time. Usually the search is further restricted to a range of pixels on the same line. Since the images are rectified, the search is done only on the same image row. Then, Stereo Matching tries to find feature correspondences between the two image feature sets. Feature detection extracts local features from the two images of the stereo pair. Firstly, the stereo image pair is rectified, which undistorts and projects the images onto a common plane. The following approach to stereo visual odometry consists of five steps. This can be solved by adding a camera, which results in a stereo camera setup. However, with this approach it is not possible to estimate scale. The cheapest solution of course is monocular visual odometry. There are many different camera setups/configurations that can be used for visual odometry, including monocular, stereo, omni-directional, and RGB-D cameras. Visual Odometry is the process of estimating the motion of a camera in real-time using successive images. It can also be used for many different applications, ranging from pose estimation, mapping, autonomous navigation to object detection and tracking and many more. As a result, this system is ideal for robots or machines that operate indoors, outdoors or both. Furthermore, one of the most striking advantages of this stereo camera technology is that it can also be used outdoors, where IR interference from sunlight renders structured-light-type sensors like the Kinect inoperable. The 12cm baseline (distance between left and right camera) results in a 0.5-20m range of depth perception, about four times higher than the widespread Kinect Depth sensors. The camera can generate VGA (100Hz) to 2K (15Hz) stereo image streams. At the same time, it provides high quality 3D point clouds, which can be used to build 3D metric maps of the environment. Advanced computer vision and geometric techniques can use depth perception to accurately estimate the 6DoF pose (x,y,z,roll,pitch,yaw) of the camera and therefore also the pose of the system it is mounted on. In addition to viewing RGB, stereovision also allows the perception of depth. The ZED Stereo Camera developed by STEREOLABS is a camera system based on the concept of human stereovision. ![]()
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