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.

In this work we propose an alternative formulation to the problem of ground reflectivity grid based localization involving laser scanned data from multiple LIDARs mounted on autonomous vehicles. The driving idea of our localization formulation is an alternative edge reflectivity grid representation which is invariant to laser source, angle of incidence, range and robot surveying motion. Such property eliminates the need of the post-factory reflectivity calibration whose time requirements are infeasible in mass produced robots/vehicles. Our experiments demonstrate that we can achieve better performance than state of the art on ground reflectivity inference-map based localization at no additional computational burden.

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