SLAM Strategy for an Autonomous Quadrotor
نویسندگان
چکیده
This report presents a solution to the Simultaneous Localization and Mapping (SLAM) problem for a quadrotor in an office environment. We have implemented a robust, efficient Correlative Scan-Matcher[2] that is able to extract rigid body constraints between both successive and non-successive poses. These pose constraints are used to generate a map via an Exactly Sparse Delayed-State Filter[1]. We present our approach and follow with experimental results for simulated data of a robot driving through the streets of Manhattan and then for a real data set generated by a ground robot with a laser scanner on the A-level of Carnegie Melon University′s Newell-Simon Hall[5].
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