The ability of autonomous vehicles (AVs) to detect three-dimensional objects is crucial for motion planning, object tracking and safe driving. This task is especially challenging for systems using only monocular cameras, for which depth estimation presents special difficulties. In this paper, we discuss the subsystem of 3D object detection in bird’s-eye-view (BEV) for a single camera in an AV system. The subsystem consists of two parts. First, it estimates the contour of the object’s projection polygon in BEV based on 2D detection and drivable area segmentation (a planar ground model is used). Second, it simplifies the object’s projection by fitting the obtained polygon to a rotated bounding box. For this part we propose a new L-shape model-based fitting algorithm. It assumes that the vertices of the input polygon belong to two adjacent sides of the fitted bounding box. We compared this algorithm with a naive approach which minimizes the bounding box’s area and with adaptations of algorithms from a paper solving a similar problem with LiDAR point clouds. The L-shape algorithm outperformed the alternatives.
We present the collections of images of the same rotating plastic object made in X-ray and visible spectra. Both parts of the dataset contain 400 images. The images are maid every 0.5 degrees of the object axial rotation. The collection of images is designed for evaluation of the performance of circular motion estimation algorithms as well as for the study of X-ray nature influence on the image analysis algorithms such as keypoints detection and description. The dataset is available at https://github.com/Visillect/xvcmdataset.
In computed tomography (CT), the relative trajectories of a sample, a detector, and a signal source are traditionally considered to be known, since they are caused by the intentional preprogrammed movement of the instrument parts. However, due to the mechanical backlashes, rotation sensor measurement errors, thermal deformations real trajectory differs from desired ones. This negatively affects the resulting quality of tomographic reconstruction. Neither the calibration nor preliminary adjustments of the device completely eliminates the inaccuracy of the trajectory but significantly increase the cost of instrument maintenance. A number of approaches to this problem are based on an automatic refinement of the source and sensor position estimate relative to the sample for each projection (at each time step) during the reconstruction process. A similar problem of position refinement while observing different images of an object from different angles is well known in robotics (particularly, in mobile robots and selfdriving vehicles) and is called Simultaneous Localization And Mapping (SLAM). The scientific novelty of this work is to consider the problem of trajectory refinement in microtomography as a SLAM problem. This is achieved by extracting Speeded Up Robust Features (SURF) features from Xray projections, filtering matches with Random Sample Consensus (RANSAC), calculating angles between projections, and using them in factor graph in combination with stepper motor control signals in order to refine rotation angles.
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