Autocalibration of lidar and optical cameras via edge alignment

Abstract

We present a new method for joint automatic extrinsic calibration and sensor fusion for a multimodal sensor system comprising a LI-DAR and an optical camera. Our approach exploits the natural alignment of depth and intensity edges when the calibration parameters are correct. Thus, in contrast to a number of existing approaches, we do not require the presence or identification of known alignment targets. On the other hand, the characteristics of each sensor modality, such as sampling pattern and information measured, are significantly different, making direct edge alignment difficult. To overcome this difficulty, we jointly fuse the data and estimate the calibration parameters. In particular, the joint processing evaluates and optimizes both the quality of edge alignment and the performance of the fusion algorithm using a common cost function on the output. We demonstrate accurate calibration in practical configurations in which depth measurements are sparse and contain no reflectivity information. Experiments on synthetic and real data obtained with a three-dimensional LIDAR sensor demonstrate the effectiveness of our approach.

A branch-and-bound algorithm for checkerboard extraction in camera-laser calibration

“…The upper bounds initially decay very fast, but their rate of decay decreases for large iterations (this is a typical characteristic of objective functions designed using the floor operator). As expected,Q computed using (11) decays faster than that of (5), showing that (11) indeed yields a tighter upper bound. The BnB method is able to find all of the inliers after 475 iterations when the tight upper bound is used and after 625 iterations when the original upper bound is used.…”

Computational Mapping of the Ground Reflectivity With Laser Scanners

“…Here, we propose edge alignments as our matching mechanism via corresponding isotropic gradient magnitudes of the prior-map and local map of ground reflectivities. A similar matching criterion was envisioned in the work of [37] for LIDAR-to-vision registration. One of the advantages of this procedure is that a post-factory reflectivity calibration process to reduce the variations in response across the multiple lasers observing the ground is not required to compute any of the global or local maps.…”

Ground-Edge-Based LIDAR Localization Without a Reflectivity Calibration for Autonomous Driving

“…Here, continuing the work of [23] we propose edge alignments as our matching mechanism via corresponding isotropic gradients of the global and local grids. A similar matching criterion was envisioned in [24] for the joint registration and LIDAR-camera fusion.…”