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Automated person detection has a wide range of applications in H/R interaction, robot nav-igation in presence of humans, pedestrian detection for Automated Driver Assistance Sys-tems(ADAS), video surveillance, content based image and video processing. But at the same time, it is one of the most challenging problems in computer vision due to the large variation of person appearances and sensor limitations. Through the years researchers have used var-ious sensors to automatically detect persons. Recently, approaches that use multiple sensors cooperatively have gained much attention as they benefit from the positive merits and different abilities of the individual sensors. Towards this end, this thesis presents a multi-modal person detection in crowded scenes from a mobile robot that uses a 2D SICK Laser Range Finder and a visual camera. A sequential approach in which the laser data is segmented to filter human leg like structures to generate person hypothesis which are further refined by a state of the art parts based visual person detector for final detection, is proposed. Integration of the implemented multi-modal person detector in our robotic platform and associated experiments are presented. Results obtained from all tests carried out have been clearly reported proving the multi-moda