R3^3SGM: Real-time Raster-Respecting Semi-Global Matching for Power-Constrained Systems

Stereo depth estimation is used for many computer vision applications. Though many popular methods strive solely for depth quality, for real-time mobile applications (e.g. prosthetic glasses or micro-UAVs), speed and power efficiency are equally, if not more, important. Many real-world systems rely on Semi-Global Matching (SGM) to achieve a good accuracy vs. speed balance, but power efficiency is hard to achieve with conventional hardware, making the use of embedded devices such as FPGAs attractive for low-power applications. However, the full SGM algorithm is ill-suited to deployment on FPGAs, and so most FPGA variants of it are partial, at the expense of accuracy. In a non-FPGA context, the accuracy of SGM has been improved by More Global Matching (MGM), which also helps tackle the streaking artifacts that afflict SGM. In this paper, we propose a novel, resource-efficient method that is inspired by MGM's techniques for improving depth quality, but which can be implemented to run in real time on a low-power FPGA. Through evaluation on multiple datasets (KITTI and Middlebury), we show that in comparison to other real-time capable stereo approaches, we can achieve a state-of-the-art balance between accuracy, power efficiency and speed, making our approach highly desirable for use in real-time systems with limited power.Comment: Accepted in FPT 2018 as Oral presentation, 8 pages, 6 figures, 4 table

Real-Time Dense Stereo Matching With ELAS on FPGA Accelerated Embedded Devices

For many applications in low-power real-time robotics, stereo cameras are the sensors of choice for depth perception as they are typically cheaper and more versatile than their active counterparts. Their biggest drawback, however, is that they do not directly sense depth maps; instead, these must be estimated through data-intensive processes. Therefore, appropriate algorithm selection plays an important role in achieving the desired performance characteristics. Motivated by applications in space and mobile robotics, we implement and evaluate a FPGA-accelerated adaptation of the ELAS algorithm. Despite offering one of the best trade-offs between efficiency and accuracy, ELAS has only been shown to run at 1.5-3 fps on a high-end CPU. Our system preserves all intriguing properties of the original algorithm, such as the slanted plane priors, but can achieve a frame rate of 47fps whilst consuming under 4W of power. Unlike previous FPGA based designs, we take advantage of both components on the CPU/FPGA System-on-Chip to showcase the strategy necessary to accelerate more complex and computationally diverse algorithms for such low power, real-time systems.Comment: 8 pages, 7 figures, 2 table

High quality framegrabber for an IR imaging camera

Diseño electrónico de un digitalizador de video para una camara de infrarrojos con potenciales aplicaciones en el campo de "Gas Sensing

Sistema de visão para aterragem automática de UAV

Dissertação para obtenção do grau de Mestre em Engenharia Electrotécnica Ramo de Automação e Electrónica IndustrialNeste estudo é proposto um sistema de visão para aterrar automaticamente um avião não tripulado (Unmanned Aerial Vehicle - UAV) comercialmente existente chamado AR4 num navio, sendo este sistema composto por uma simples câmara RGB (espectro visível). A aplicação prevê a sua colocação no convés de um navio para estimar a pose do UAV (posição 3D e orientação) durante o processo de aterragem. Ao utilizar um sistema de visão localizado no navio permite a utilização de um UAV com menos poder de processamento, reduzindo assim o seu tamanho e peso. O método proposto utiliza uma abordagem baseada no modelo 3D do objeto em que é necessária a utilização do modelo CAD 3D do UAV. A pose é estimada utilizando uma arquitetura baseada num filtro de partículas. A implementação utilizada é baseada nas estratégias de evolução presentes nos algoritmos genéticos, evitando assim perda de diversidade nas possibilidades criadas. Também é implementada filtragem temporal entre frames - filtro de Kalman unscented - por forma a obter uma melhor estimativa de pose. Os resultados mostram erros angulares e de posição compatíveis com o sistema de aterragem automática. O algoritmo é apropriado para aplicações em tempo real em standard workstations, com unidades de processamento gráfico. O UAV vai operar de um navio patrulha pertencente à Marinha de Guerra Portuguesa, o que implica a capacidade de aterrar num navio de 27 metros de comprimento, 5,9 metros de boca, com uma zona de aterragem pequena e irregular de 5x6 metros localizada na proa do navio. A implementação de um sistema completamente autónomo é muito importante em cenários reais, uma vez que estes navios têm uma guarnição limitada e os pilotos de UAV nem sempre se encontram disponíveis. Além disso, um sistema de visão é mais robusto em ambientes onde pode ocorrer empastelamento ao sinal GPS.Abstract: In this study a vision system for autonomous landing of an existing commercial aerial vehicle (UAV) named AR4 aboard a ship, based on a single standard RGB digital camera is proposed. The envisaged application is of ground-based automatic landing, where the vision system is located on the ship’s deck and is used to estimate the UAV pose (3D position and orientation) during the landing process. Using a vision system located on the ship makes it possible to use an UAV with less processing power, decreasing its size and weight. The proposed method uses a 3D model based pose estimation approach that requires the 3D CAD model of the UAV. Pose is estimated using a particle filtering framework. The implemented particle filter is inspired in the evolution strategies present in the genetic algorithms avoiding sample impoverishment. Temporal filtering is also implemented between frames – unscented Kalman filter – in order to get a better pose estimation. Results show that position and angular errors are compatible with automatic landing system requirements. The algorithm is suitable for real time implementation in standard workstations with graphical processing units. The UAV will operate from the Portuguese Navy fast patrol boats (FPB), which implies the capability of landing in 27 m length, 5.9 m breadth vessels, with a 5x6 m small and irregular landing zone located at the boat´s stern. The implementation of a completely autonomous system is very important in real scenarios,since this ships have only a small crew and UAV pilots are not usually available. Moreover a vision based system is more robust in an environment where GPS jamming can occur