Air Force Institute of Technology Research Report 2020

This Research Report presents the FY20 research statistics and contributions of the Graduate School of Engineering and Management (EN) at AFIT. AFIT research interests and faculty expertise cover a broad spectrum of technical areas related to USAF needs, as reflected by the range of topics addressed in the faculty and student publications listed in this report. In most cases, the research work reported herein is directly sponsored by one or more USAF or DOD agencies. AFIT welcomes the opportunity to conduct research on additional topics of interest to the USAF, DOD, and other federal organizations when adequate manpower and financial resources are available and/or provided by a sponsor. In addition, AFIT provides research collaboration and technology transfer benefits to the public through Cooperative Research and Development Agreements (CRADAs). Interested individuals may discuss ideas for new research collaborations, potential CRADAs, or research proposals with individual faculty using the contact information in this document

Obstacle Detection and Avoidance System Based on Monocular Camera and Size Expansion Algorithm for UAVs

One of the most challenging problems in the domain of autonomous aerial vehicles is the designing of a robust real-time obstacle detection and avoidance system. This problem is complex, especially for the micro and small aerial vehicles, that is due to the Size, Weight and Power (SWaP) constraints. Therefore, using lightweight sensors (i.e., Digital camera) can be the best choice comparing with other sensors; such as laser or radar. For real-time applications, different works are based on stereo cameras in order to obtain a 3D model of the obstacles, or to estimate their depth. Instead, in this paper, a method that mimics the human behavior of detecting the collision state of the approaching obstacles using monocular camera is proposed. The key of the proposed algorithm is to analyze the size changes of the detected feature points, combined with the expansion ratios of the convex hull constructed around the detected feature points from consecutive frames. During the Aerial Vehicle (UAV) motion, the detection algorithm estimates the changes in the size of the area of the approaching obstacles. First, the method detects the feature points of the obstacles, then extracts the obstacles that have the probability of getting close toward the UAV. Secondly, by comparing the area ratio of the obstacle and the position of the UAV, the method decides if the detected obstacle may cause a collision. Finally, by estimating the obstacle 2D position in the image and combining with the tracked waypoints, the UAV performs the avoidance maneuver. The proposed algorithm was evaluated by performing real indoor and outdoor flights, and the obtained results show the accuracy of the proposed algorithm compared with other related works.Research supported by the Spanish Government through the Cicyt project ADAS ROAD-EYE (TRA2013-48314-C3-1-R)

Point Normal Orientation and Surface Reconstruction by Incorporating Isovalue Constraints to Poisson Equation

Oriented normals are common pre-requisites for many geometric algorithms based on point clouds, such as Poisson surface reconstruction. However, it is not trivial to obtain a consistent orientation. In this work, we bridge orientation and reconstruction in implicit space and propose a novel approach to orient point clouds by incorporating isovalue constraints to the Poisson equation. Feeding a well-oriented point cloud into a reconstruction approach, the indicator function values of the sample points should be close to the isovalue. Based on this observation and the Poisson equation, we propose an optimization formulation that combines isovalue constraints with local consistency requirements for normals. We optimize normals and implicit functions simultaneously and solve for a globally consistent orientation. Owing to the sparsity of the linear system, an average laptop can be used to run our method within reasonable time. Experiments show that our method can achieve high performance in non-uniform and noisy data and manage varying sampling densities, artifacts, multiple connected components, and nested surfaces

ReSimAD: Zero-Shot 3D Domain Transfer for Autonomous Driving with Source Reconstruction and Target Simulation

Domain shifts such as sensor type changes and geographical situation variations are prevalent in Autonomous Driving (AD), which poses a challenge since AD model relying on the previous-domain knowledge can be hardly directly deployed to a new domain without additional costs. In this paper, we provide a new perspective and approach of alleviating the domain shifts, by proposing a Reconstruction-Simulation-Perception (ReSimAD) scheme. Specifically, the implicit reconstruction process is based on the knowledge from the previous old domain, aiming to convert the domain-related knowledge into domain-invariant representations, e.g., 3D scene-level meshes. Besides, the point clouds simulation process of multiple new domains is conditioned on the above reconstructed 3D meshes, where the target-domain-like simulation samples can be obtained, thus reducing the cost of collecting and annotating new-domain data for the subsequent perception process. For experiments, we consider different cross-domain situations such as Waymo-to-KITTI, Waymo-to-nuScenes, Waymo-to-ONCE, etc, to verify the zero-shot target-domain perception using ReSimAD. Results demonstrate that our method is beneficial to boost the domain generalization ability, even promising for 3D pre-training.Comment: Code and simulated points are available at https://github.com/PJLab-ADG/3DTrans#resima

Object Tracking in Video Sequences

Tässä diplomityössä vertaillaan konenäössä käytettyjen SIFT-, SURF- ja ORB-algoritmia objektin seurannassa. Tutkimuksen tavoitteena on tarkastella algoritmien soveltuvuutta erityyppisille videoille ja erilaisiin käyttötarkoituksiin, kuten reaaliaikaisiin järjestelmiin, mutta myös järjestelmiin, joissa reaaliaikaisuus ei ole vaatimuksena. Algoritmeja on aikaisemmissa tutkimuksissa vertailtu kuvaparien avulla, mutta tutkimuksia objektin seurannasta SIFT-, SURF- ja ORB-algoritmeja käyttäen ei löytynyt. SIFT- ja SURF-algoritmien vertailu niitä uudemman ORB-algormitmin kanssa tuo lisäksi uutta tietoa sen suorituskyvystä. Tutkimukset ovat olleet ORB:n osalta vielä vähäisiä. Vertailu tehdään neljän eri testivideon avulla algoritmien vakioparametreilla ja optimoiduilla parametreilla. Vertailussa otetaan huomioon algoritmien tarkkuus, nopeus, sekä sietokyky skaalaus-, rotaatio- ja kuvakulmamuutoksille. Testiympäristössä käytettiin Python-ohjelmointikieltä ja konenäköön suunnattua OpenCV-kirjastoa. Tuloksista selviää, että kaikki kolme algoritmia soveltuvat objektin seuraamiseen. Algoritmin valinta kuitenkin riippuu käyttökohteesta ja videon ominaisuuksista. Erityisesti ORB:n kohdalla tarkkuus parani merkittävästi optimoiduilla parametreilla. SIFT:n ja SURF:n tarkkuutta ei optimoinnilla juurikaan saatu parannettua, mutta niiden laskenta-aika lyheni. Algoritmeistä ORB oli jokaisessa videossa nopein ja SIFT keskiarvollisesti tarkin. Laskenta-ajallisesti SURF oli algoritmeista hitain, mikä voi rajottaa sen käyttöä. Tulosten perusteella ORB:n käyttöä voidaan suositella käytettäväksi reaaliaikaisissa järjestelmissä optimoiduilla parametreilla ja SIFT:n käyttöä puolestaan tarkempaan seurantaan. SURF:n tarkkuus oli paras tapauksissa, joissa videokuva oli heilahtanut, joten sen käyttöä voidaan suositella kyseisissä tilanteissa

Air Force Institute of Technology Research Report 2019

This Research Report presents the FY19 research statistics and contributions of the Graduate School of Engineering and Management (EN) at AFIT. AFIT research interests and faculty expertise cover a broad spectrum of technical areas related to USAF needs, as reflected by the range of topics addressed in the faculty and student publications listed in this report. In most cases, the research work reported herein is directly sponsored by one or more USAF or DOD agencies. AFIT welcomes the opportunity to conduct research on additional topics of interest to the USAF, DOD, and other federal organizations when adequate manpower and financial resources are available and/or provided by a sponsor. In addition, AFIT provides research collaboration and technology transfer benefits to the public through Cooperative Research and Development Agreements (CRADAs). Interested individuals may discuss ideas for new research collaborations, potential CRADAs, or research proposals with individual faculty using the contact information in this document