Onboard Localization of an Unmanned Aerial Vehicle in an Unknown Environment

Tato práce se zabývá onboard lokalizací bezpilotního letounu bez přístupu k službám globálních navigačhních systémů. Hlavní cíl této práce spočívá v návrhu a implementaci metody pro simultánní lokalizaci a mapování, která využívá laserové skeny z rotačního laserového dálkoměru k odhadování pozice letounu. Byla implementována technika pro odhadování posunutí a rotace mezi dvěma laserovými skeny pomocí zarovnání korespondujících měření ze zmíněných laserových skenů. Navržené řešení zahrnuje fúzi odhadu pozice z inercialní měřicí jednotky, relativní posunutí získané ze zarovnání po sobě jdoucích skenů a absolutní pozice získané ze zarovnání laserových skenů do postupně stavěné mapy. Fúzovaný odhad pozice uzavírá vnější zpětnovazební smyčku prediktivního řízení. Vyvinutý systém je nejprve posouzen v simulaci a poté jsou jeho schopnosti předvedeny na sadě hardwarových experimentů s reálným dronem.This thesis is concerned with onboard localization of an unmanned aerial vehicle without the access to global navigation satellite system services. The central focus of this work lies in design and implementation of simultaneous localization and mapping method that uses laser scans from a rotating laser rangefinder to estimate the position of the vehicle. A scan matching technique was implemented to estimate the displacement and rotation between two laser scans by aligning corresponding measurements from the two scans. The proposed solution involves fusion of position estimate from the inertial measurement unit, the relative displacement obtained by aligning successive laser scans, and the absolute position obtained by aligning laser scans into the gradually built map. The fused position estimate closes the outer feedback loop of the model predictive control. The developed system is first evaluated in simulations, and then its capabilities are demonstrated on a set of hardware experiments with a real drone

Data-driven Policy Transfer with Imprecise Perception Simulation

The paper presents a complete pipeline for learning continuous motion control policies for a mobile robot when only a non-differentiable physics simulator of robot-terrain interactions is available. The multi-modal state estimation of the robot is also complex and difficult to simulate, so we simultaneously learn a generative model which refines simulator outputs. We propose a coarse-to-fine learning paradigm, where the coarse motion planning is alternated with imitation learning and policy transfer to the real robot. The policy is jointly optimized with the generative model. We evaluate the method on a real-world platform in a batch of experiments.Comment: Submitted to IROS 2018 with RAL optio

Present and Future of SLAM in Extreme Underground Environments

This paper reports on the state of the art in underground SLAM by discussing different SLAM strategies and results across six teams that participated in the three-year-long SubT competition. In particular, the paper has four main goals. First, we review the algorithms, architectures, and systems adopted by the teams; particular emphasis is put on lidar-centric SLAM solutions (the go-to approach for virtually all teams in the competition), heterogeneous multi-robot operation (including both aerial and ground robots), and real-world underground operation (from the presence of obscurants to the need to handle tight computational constraints). We do not shy away from discussing the dirty details behind the different SubT SLAM systems, which are often omitted from technical papers. Second, we discuss the maturity of the field by highlighting what is possible with the current SLAM systems and what we believe is within reach with some good systems engineering. Third, we outline what we believe are fundamental open problems, that are likely to require further research to break through. Finally, we provide a list of open-source SLAM implementations and datasets that have been produced during the SubT challenge and related efforts, and constitute a useful resource for researchers and practitioners.Comment: 21 pages including references. This survey paper is submitted to IEEE Transactions on Robotics for pre-approva