3 research outputs found
Onboard Localization of an Unmanned Aerial Vehicle in an Unknown Environment
Tato práce se zabĂ˝vá onboard lokalizacĂ bezpilotnĂho letounu bez pĹ™Ăstupu k sluĹľbám globálnĂch navigaÄŤhnĂch systĂ©mĹŻ. HlavnĂ cĂl tĂ©to práce spoÄŤĂvá v návrhu a implementaci metody pro simultánnĂ lokalizaci a mapovánĂ, která vyuĹľĂvá laserovĂ© skeny z rotaÄŤnĂho laserovĂ©ho dálkomÄ›ru k odhadovánĂ pozice letounu. Byla implementována technika pro odhadovánĂ posunutĂ a rotace mezi dvÄ›ma laserovĂ˝mi skeny pomocĂ zarovnánĂ korespondujĂcĂch měřenĂ ze zmĂnÄ›nĂ˝ch laserovĂ˝ch skenĹŻ. NavrĹľenĂ© Ĺ™ešenĂ zahrnuje fĂşzi odhadu pozice z inercialnĂ měřicĂ jednotky, relativnĂ posunutĂ zĂskanĂ© ze zarovnánĂ po sobÄ› jdoucĂch skenĹŻ a absolutnĂ pozice zĂskanĂ© ze zarovnánĂ laserovĂ˝ch skenĹŻ do postupnÄ› stavÄ›nĂ© mapy. FĂşzovanĂ˝ odhad pozice uzavĂrá vnÄ›jšà zpÄ›tnovazebnĂ smyÄŤku prediktivnĂho Ĺ™ĂzenĂ. VyvinutĂ˝ systĂ©m je nejprve posouzen v simulaci a potĂ© jsou jeho schopnosti pĹ™edvedeny na sadÄ› hardwarovĂ˝ch experimentĹŻ s reálnĂ˝m 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