Print Email Facebook Twitter Low-cost stereo-based obstacle avoidance for small UAVs using uncertainty maps Title Low-cost stereo-based obstacle avoidance for small UAVs using uncertainty maps Author Lyrakis, Alex (TU Delft Electrical Engineering, Mathematics and Computer Science) Contributor van Dijk, Tom (mentor) de Croon, G.C.H.E. (mentor) Wong, J.S.S.M. (mentor) Verhoeven, C.J.M. (graduation committee) van Genderen, A.J. (graduation committee) Degree granting institution Delft University of Technology Programme Computer Engineering Date 2019-11-22 Abstract The last years there is a wide interest in UAVs which can be attributed to their low cost and wide range of use in recreational, commercial and scientific applications. Despite the large increase in drones, UAV flights are permitted only in secluded areas. In order to be granted access to public areas, it must prove its capacity to sense and safely avoid collisions with other obstacles. Therefore, the need for a secure and reliable CAS is imperative. In this thesis alow-cost, low computationally demanding, stereo-based, robust CAS solution for small UAVs is designed, assuming flights primarily in an outdoor environment. In order to address this problem, firstly the existing dense stereo open-source algorithms are reviewed based on their suitability for obstacle avoidance and their computational complexity. Based on a semantic evaluation and profiling, the review concludes that BM should be preferred for low-cost obstacle avoidance and SGBM should be used only in highly textureless environments. Subsequently, since the imperfect accuracy of any existing stereo solution is a fact, a machine learning method is introduced in order to predict the uncertainty of the stereo measurements. This so called “uncertainty map” method assigns an uncertainty value to every image pixel. It was shown that it can successfully predict the uncertainties of BM and SGBM stereo algorithms. Furthermore, a low-cost collision avoidance method was proposed which makes use of uncertainty map in sensing filtering, collision detection and path planning. The evaluation showed that the use of uncertainty map improvesboth collision detection and path planning, especially when BM is used. Last but not least, the whole CAS was implemented in an embedded system Raspberry Pi 3 model B+. Results show that real-time execution of the propsoed CAS with a runtime frequency of 4-54 Hz is possible when BM is used and 1-16 Hz when SGBM is used. Subject StereoCollision AvoidanceMappingPath PlanningUncertainty MapLow-costRaspberry PiImage-space To reference this document use: http://resolver.tudelft.nl/uuid:96a87f24-c9b1-4a3e-99df-09dc27772609 Embargo date 2021-11-15 Part of collection Student theses Document type master thesis Rights © 2019 Alex Lyrakis Files PDF Alexios_Lyrakis_MSc_thesis.pdf 12.79 MB Close viewer /islandora/object/uuid:96a87f24-c9b1-4a3e-99df-09dc27772609/datastream/OBJ/view