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Under the three-year Video Surveillance and Monitoring (VSAM) project (1997--1999), the Robotics Institute at Carnegie Mellon University (CMU) and the Sarnoff Corporation developed a system for autonomous Video Surveillance and Monitoring. The technical approach uses multiple, cooperative video sensors to provide continuous coverage of people and vehicles in a cluttered environment. This final report presents an overview of the system, and of the technical accomplishments that have been achieved. c fl2000 Carnegie Mellon University This work was funded by the DARPA Image Understanding under contract DAAB07-97-C-J031, and by the Office of Naval Research under grant N00014-99-1-0646. 1 Introduction The thrust of CMU research under the DARPA Video Surveillance and Monitoring (VSAM) project is cooperative multi-sensor surveillance to support battlefield awareness [17]. Under our VSAM Integrated Feasibility Demonstration (IFD) contract, we have developed automated video understandi...

Abstract—This paper is about automatic calibration of a camera-lidar system. The method presented is designed to be as general as possible allowing it to be used in a large range of systems and applications. The approach uses normalized mutual information to compare camera images with lidar scans

Abstract This paper addresses the problem of estimating the intrinsic parameters of the 3D Velodyne lidar while at the same time computing its extrinsic calibration with respect to a rigidly connected camera. Existing approaches to solve this nonlinear estimation problem are based on iterative

Ph.D. Dissertation2i A mi amor, por que su valentía me ha enseñado como recorrer este camino. iiContents

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préparée à CMLA, ENS-Cachan

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-measuring these values is tedious and inaccurate. In addition, autonomous robots can be rendered inoperable if their sensors ’ calibrations change over time. Yet previously published camera-laser calibration algorithms are offline only, requiring significant amounts of data and/or specific calibration targets