Algorithm for Mobile Security Platform Employment in an Unknown Indoor Environment

Abstract

Designing and fabrication of mobile security platform is a relatively new development in security conscious world. These mobile security platforms are being developed keeping in view their employment environment. In general, these platforms perform two main tasks, first is the basic task of mapping the environment in which it will operate as well as self localization and thereafter navigation in that particular environment, second is the specific security task of surveillance. Mapping and localization has become one of the mainstream research areas in mobile robotics and is generally performed using simultaneous localization & mapping (SLAM) algorithms. These SLAM algorithm are broadly classified in three categories, first is based on calculation methods like Kalman Filter or the particle filter, hybrid comprising of both filters and graph-based, second is based on sensors like vision, range measurement devices and odometry, third is based on structure like online SLAM and full SLAM. Surveillance task is performed by assessment algorithms supported by different sensors like acoustic, vibration, passive infrared, microwave, optical, ultrasonic and vision. This work focuses on mapping and localization part of a mobile security platform which has to be employed in an unknown indoor environment. In this context, an in-depth study has been carried out on SLAM algorithms developed to-date along with different sensors available. Visual SLAM algorithm is tested with latest ZED stereo camera in an unknown indoor environment for its use in mobile security platform.