24 research outputs found
Accurate Calibration of Multi-LiDAR-Multi-Camera Systems
As autonomous driving attracts more and more attention these days, the algorithms and sensors used for machine perception become popular in research, as well. This paper investigates the extrinsic calibration of two frequently-applied sensors: the camera and Light Detection and Ranging (LiDAR). The calibration can be done with the help of ordinary boxes. It contains an iterative refinement step, which is proven to converge to the box in the LiDAR point cloud, and can be used for system calibration containing multiple LiDARs and cameras. For that purpose, a bundle adjustment-like minimization is also presented. The accuracy of the method is evaluated on both synthetic and real-world data, outperforming the state-of-the-art techniques. The method is general in the sense that it is both LiDAR and camera-type independent, and only the intrinsic camera parameters have to be known. Finally, a method for determining the 2D bounding box of the car chassis from LiDAR point clouds is also presented in order to determine the car body border with respect to the calibrated sensors
A Brief Survey of Image-Based Depth Upsampling
Recently, there has been remarkable growth of interest in the development and applications of Time-of-Flight (ToF) depth cameras. However, despite the permanent improvement of their characteristics, the practical applicability of ToF cameras is still limited by low resolution and quality of depth measurements. This has motivated many researchers to combine ToF cameras with other sensors in order to enhance and upsample depth images. In this paper, we compare ToF cameras to three image-based techniques for depth recovery, discuss the upsampling problem and survey the approaches that couple ToF depth images with high-resolution optical images. Other classes of upsampling methods are also mentioned
Optimal Multi-view Correction of Local Affine Frames
The technique requires the epipolar geometry to be pre-estimated between each
image pair. It exploits the constraints which the camera movement implies, in
order to apply a closed-form correction to the parameters of the input
affinities. Also, it is shown that the rotations and scales obtained by
partially affine-covariant detectors, e.g., AKAZE or SIFT, can be completed to
be full affine frames by the proposed algorithm. It is validated both in
synthetic experiments and on publicly available real-world datasets that the
method always improves the output of the evaluated affine-covariant feature
detectors. As a by-product, these detectors are compared and the ones obtaining
the most accurate affine frames are reported. For demonstrating the
applicability, we show that the proposed technique as a pre-processing step
improves the accuracy of pose estimation for a camera rig, surface normal and
homography estimation