In this paper, we propose a simple way to utilize stereo camera data to improve feature descriptors. Computer vision algorithms that use a stereo camera require some calculations of 3D information. We leverage this pre-calculated information to improve feature descriptor algorithms. We use the 3D feature information to estimate the scale of each feature. This way, each feature descriptor will be more robust to scale change without significant computations. In addition, we use stereo images to construct the descriptor vector. The SIFT and FREAK descriptors are used to evaluate the proposed method. The scale normalization technique in feature tracking test improves the standard SIFT by 8.75 and improves the standard FREAK by 28.65 . We also use a simple visual odometry algorithm and test it on the KITTI datasets. The stereo FREAK descriptor raises the number of inlier matches by 19 and consequently improves the accuracy of visual odometry by 23 .
A complete overview on visual odometry can be found in @cite_6 and @cite_3 . Feature descriptors have been used in visual odometry for many years. But descriptors became more important in this application after @cite_18 . They suggested using descriptors to match features between the left and right camera images and by using them to track features over time. This technique has recently become more popular and consequently, computing feature descriptors has become one of the major steps in the success of a visual odometry algorithm.
@cite_18 @cite_3 @cite_6
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Applications for real-time visual tracking can be found in many areas, including visual odometry and augmented reality. Interest point detection and feature description form the basis of feature-based tracking, and a variety of algorithms for these tasks have been proposed. In this work, we present (1) a carefully designed dataset of video sequences of planar textures with ground truth, which includes various geometric changes, lighting conditions, and levels of motion blur, and which may serve as a testbed for a variety of tracking-related problems, and (2) a comprehensive quantitative evaluation of detector-descriptor-based visual camera tracking based on this testbed. We evaluate the impact of individual algorithm parameters, compare algorithms for both detection and description in isolation, as well as all detector-descriptor combinations as a tracking solution. In contrast to existing evaluations, which aim at different tasks such as object recognition and have limited validity for visual tracking, our evaluation is geared towards this application in all relevant factors (performance measures, testbed, candidate algorithms). To our knowledge, this is the first work that comprehensively compares these algorithms in this context, and in particular, on video streams. This paper describes a visual feature detector and descriptor scheme designed to address the specific problems of humanoid robots in the tasks of visual odometry, localization, and SLAM (Simultaneous Localization And Mapping). During walking, turning, and squatting movements, the camera of a humanoid robot moves in jerky and sometimes unpredictable way. This causes an undesired motion blur in the images grabbed by the robot camera, that negatively affects the performance of the image processing algorithms. Indeed, the classical features detector and descriptor filtering techniques, that proved to work so well for wheeled robots, do not perform so reliably in humanoid robots. This paper presents a method to detect image interest points (invariant to scale transformation and rotations) robust to motion-blur introduced by the camera motion. Our approach is based on a preprocessing step to estimate the point spread function (PSF) of the motion blur. The PSF is used to deconvolve the image reducing the blur. Then, we apply a feature detector inspired by SURF approach and the feature descriptor from SIFT. Experiments performed on standard datasets corrupted with motion blur and on images taken by a camera mounted on a small humanoid robot show the effectiveness of the proposed technique. Our approach presents higher performances and higher reliability in matching features in the different images of a sequence affected by motion-blur. This paper presents a visual-inertial odometry framework that tightly fuses inertial measurements with visual data from one or more cameras, by means of an iterated extended Kalman filter. By employing image patches as landmark descriptors, a photometric error is derived, which is directly integrated as an innovation term in the filter update step. Consequently, the data association is an inherent part of the estimation process and no additional feature extraction or matching processes are required. Furthermore, it enables the tracking of noncorner-shaped features, such as lines, and thereby increases the set of possible landmarks. The filter state is formulated in a fully robocentric fashion, which reduces errors related to nonlinearities. This also includes partitioning of a landmarks location estimate into a bearing vector and distance and thereby allows an undelayed initialization of landmarks. Overall, this results in a compact approach, which exhibits a high level of robustness with respect to low ...