tailieunhanh - Báo cáo hóa học: " Radar SLAM using visual features"

Tuyển tập các báo cáo nghiên cứu về hóa học được đăng trên tạp chí hóa hoc quốc tế đề tài : Radar SLAM using visual features | Callmer et al. EURASIP Journal on Advances in Signal Processing 2011 2011 71 http content 2011 1 71 o EURASIP Journal on Advances in Signal Processing a SpringerOpen Journal RESEARCH Open Access Radar SLAM using visual features Jonas Callmer1 David Tornqvist1 Fredrik Gustafsson1 Henrik Svensson2 and Pelle Carlbom3 Abstract A vessel navigating in a critical environment such as an archipelago requires very accurate movement estimates. Intentional or unintentional jamming makes GPS unreliable as the only source of information and an additional independent supporting navigation system should be used. In this paper we suggest estimating the vessel movements using a sequence of radar images from the preexisting body-fixed radar. Island landmarks in the radar scans are tracked between multiple scans using visual features. This provides information not only about the position of the vessel but also of its course and velocity. We present here a navigation framework that requires no additional hardware than the already existing naval radar sensor. Experiments show that visual radar features can be used to accurately estimate the vessel trajectory over an extensive data set. I. Introduction In autonomous robotics there is a need to accurately estimate the movements of a vehicle. A simple movement sensor like a wheel encoder on a ground robot or a pit log on a vessel will under ideal circumstances provide quite accurate movement measurements. Unfortunately they are sensitive to disturbances. For example wheel slip due to a wet surface will be interpreted incorrectly by a wheel encoder and strong currents will not be correctly registered by the pit log why a position estimate based solely on these sensors will drift off. In applications like autonomous robotics the movement accuracy needs to be high why other redundant movement measurement methods are required. A common approach is to study the surroundings and see how they change over time. By relating