Real-time Embedded Platform Research Articles

LSVL: Large-scale season-invariant visual localization for UAVs

Localization of autonomous unmanned aerial vehicles (UAVs) relies heavily on Global Navigation Satellite Systems (GNSS), which are susceptible to interference. Especially in security applications, robust localization algorithms independent of GNSS are needed to provide dependable operations of autonomous UAVs also in interfered conditions. Typical non-GNSS visual localization approaches rely on known starting pose, work only on a small-sized map, or require known flight paths before a mission starts. We consider the problem of localization with no information on initial pose or planned flight path. We propose a solution for global visual localization on large maps, based on matching orthoprojected UAV images to satellite imagery using learned season-invariant descriptors, and test with environment sizes up to 100 km2. We show that the method is able to determine heading, latitude and longitude of the UAV at 12.6–18.7 m lateral translation error in as few as 23.2–44.4 updates from an uninformed initialization, also in situations of significant seasonal appearance difference (winter–summer) between the UAV image and the map. We evaluate the characteristics of multiple neural network architectures for generating the descriptors, and likelihood estimation methods that are able to provide fast convergence and low localization error. We also evaluate the operation of the algorithm using real UAV data and evaluate running time on a real-time embedded platform. We believe this is the first work that is able to recover the pose of an UAV at this scale and rate of convergence, while allowing significant seasonal difference between camera observations and map.

RoadNet: An 80-mW Hardware Accelerator for Road Detection

As a fundamental feature of intelligent vehicles, vision-based road detection must be executed on a real-time embedded platform with high accuracy. Road detection is often applied in conjunction with lane detection to determine the drivable regions. Although some existing research based on large deep learning models achieved high accuracy using the road detection dataset, they often did not consider the low power requirement of a typical embedded system. In this letter, an ultralow-power hardware accelerator for road detection is proposed. By adopting a top-down convolutional neural network (CNN) structure, a small CNN, namely RoadNet, is trained that can achieve near state-of-the-art detection accuracy. Furthermore, each CNN layer is trimmed to be computationally identical and every processing element in the architecture is fully utilized. When implemented using 32-nm process technology, the proposed hardware accelerator requires the chip area of 0.45 mm2 and the power consumption of only 80 mW, which results in an equivalent power efficiency of about 300 GOP/s/W. The RoadNet chip is capable of processing 241 frames/s at 1080P image resolution. It stands out as an ultralow-power hardware accelerator in an embedded system for road detection.

THE RAPID DEVELOPMENT OF A CLOSED-LOOP CONTROL SYSTEM

In this paper, the methods to rapidly deploy closed-loop control systems are presented. A flexible real-time embedded platform based on reconfigurable computing technologies is established, on which control blocks consisting of optimized control algorithms are set up. By employing control blocks, a set of tools aiming to shorten the development cycle of embedded control systems are developed. Compare to conventional ways, the tools give controller developers much faster ways to construct required controllers with higher flexibility.