Swarms of unmanned aerial vehicles — A survey

An ever wider variety of applications employ Single Photon Avalanche Diodes (SPADs) for the detection of faint optical signals. SPADs are p-n junction biased above the breakdown voltage and operate in Geiger-mode: each electron-hole pair can trigger an avalanche multiplication process that causes the current to swiftly rise to its final value. Additional quenching electronics is necessary for a SPAD proper working. The additional electronics characteristics directly affect the system's obtainable performances. Different quenching circuits affect the detector performances in different ways. In the last 15 years there has been considerable development in the integration of the quenching circuitry directly with the detector, thus leading to improved performances. This paper reviews the state of the art of this evolution, examining and comparing different classes of quenching circuits and explaining their mode of operations, their advantages and disadvantages.

http://www.sensl.com/downloads/irp/2010_Gallivanoni_Quenching_Circuits_SPAD.pdf

Progress in Quenching Circuits for Single Photon Avalanche Diodes

The development of a navigation system is one of the major challenges in building a fully autonomous platform. Full autonomy requires a dependable navigation capability not only in a perfect situation with clear GPS signals but also in situations, where the GPS is unreliable. Therefore, self-contained odometry systems have attracted much attention recently. This paper provides a general and comprehensive overview of the state of the art in the field of self-contained, i.e., GPS denied odometry systems, and identifies the out-coming challenges that demand further research in future. Self-contained odometry methods are categorized into five main types, i.e., wheel, inertial, laser, radar, and visual, where such categorization is based on the type of the sensor data being used for the odometry. Most of the research in the field is focused on analyzing the sensor data exhaustively or partially to extract the vehicle pose. Different combinations and fusions of sensor data in a tightly/loosely coupled manner and with filtering or optimizing fusion method have been investigated. We analyze the advantages and weaknesses of each approach in terms of different evaluation metrics, such as performance, response time, energy efficiency, and accuracy, which can be a useful guideline for researchers and engineers in the field. In the end, some future research challenges in the field are discussed.

/pdf/a-survey-on-odometry-for-autonomous-navigation-systems-zfru93bmxo.pdf

A Survey on Odometry for Autonomous Navigation Systems

The design of a low-light level pixel in CMOS technology for biomedical applications is described. This pixel is also suitable for very high-speed applications, such as fluorescence lifetime imaging (FLIM) used for drug discovery and/or minimally-invasive optical biopsy. In order to achieve high-speed imaging using single-photon detection, a detector with a very low dead-time is needed. The single-photon avalanche-photodiode (SPAD) discussed in this work uses a mainstream deep-submicron CMOS technology in order to achieve ultrahigh-speed operation and high pixel fill-factor, with in-pixel active quench and reset circuits. The paper also presents an innovative approach for reducing the deadtime of the detector and an attractive technique for simultaneous high-speed image acquisition by all the pixels of an array in parallel.

High-Speed, Single-Photon Avalanche-Photodiode Imager for Biomedical Applications

http://vislab.ucr.edu/PUBLICATIONS/pubs/Journal%20and%20Conference%20Papers/after10-1-1997/Journals/2014/Efficient%20smile%20detection%20by%20Extreme%20Learning%20Machine14.pdf

Efficient smile detection by Extreme Learning Machine

Streak detection and analysis pipeline for space-debris optical images

The research reported in this paper focuses on the modeling of Local Binary Patterns (LBPs) and presents an a
priori model where LBPs are considered as combinations of permutations. The aim is to increase the understanding of the
mechanisms related to the formation of uniform LBPs. Uniform patterns are known to exhibit high discriminative
capability; however, so far the reasons for this have not been fully explored. We report an observation that although the overall a
priori probability of uniform LBPs is high, it is mostly due to the high probability of only certain classes of patterns, while
the a priori probability of other patterns is very low. In order to examine this behavior, the relationship between the runs up
and down test for randomness of permutations and the uniform LBPs was studied. Quantitative experiments were then carried
out to show that the relative effect of uniform patterns to the LBP histogram is strengthened with deterministic data, in comparison
with the i.i.d. model. This was verified by using an a priori model as well as through experiments with natural image data. It was further illustrated that specific uniform LBP codes can also provide responses to salient shapes, that is, to monotonically changing intensity functions and edges within the image microstructure.

/pdf/towards-understanding-the-formation-of-uniform-local-binary-2wd01r2lq4.pdf

Towards Understanding the Formation of Uniform Local Binary Patterns

Robust real-time object detection and tracking are challenging problems in autonomous transportation systems due to operation of algorithms in inherently uncertain and dynamic environments and rapid movement of objects. Therefore, tracking and detection algorithms must cooperate with each other to achieve smooth tracking of detected objects that later can be used by the navigation system. In this paper, we first present an efficient multi-sensor fusion approach based on the probabilistic data association method in order to achieve accurate object detection and tracking results. The proposed approach fuses the detection results obtained independently from four main sensors: radar, LiDAR, RGB camera and infrared camera. It generates object region proposals based on the fused detection result. Then, a Convolutional Neural Network (CNN) approach is used to identify the object categories within these regions. The CNN is trained on a real dataset from different ferry driving scenarios. The experimental results of tracking and classification on real datasets show that the proposed approach provides reliable object detection and classification results in maritime environments.

An Efficient Multi-sensor Fusion Approach for Object Detection in Maritime Environments

This chapter describes MIPA4k, a 64 ×64 cell mixed-mode image processor array chip Each cell includes an image sensor, A/D/A conversion, embedded digital and analog memories, and hardware-optimized grey-scale and binary processing cores We describe the architecture of the processor cell, go through the different functional blocks and explore its processing capabilities The processing capabilities of the cells include programmable space-dependent neighbourhood connections, ranked-order filtering, rank identification and anisotropic resistive filtering For example, asynchronous analog morphological reconstruction operation can be performed with MIPA4k The image sensor has an option for locally adaptive exposure time Also, the peripheral circuitry can highlight windows of activation, and pattern matching can be performed on these regions of interest (ROI) with the aid of parallel write operation to the active window As the processing capabilities are complemented with global OR and global sum operations, MIPA4k is an effective tool for high-speed image analysis

MIPA4k: Mixed-Mode Cellular Processor Array

In this paper, we propose an effective object detection framework based on proposal fusion of multiple sensors such as infrared camera, RGB cameras, radar and LiDAR. Our framework first applies the Selective Search (SS) method on RGB image data to extract possible candidate proposals which likely contain the objects of interest. Then it uses the information from other sensors in order to reduce the number of generated proposals by SS and find more dense proposals. Finally, the class of objects within the final proposals are identified by Convolutional Neural Network (CNN). Experimental results on real dataset demonstrate that our framework can precisely detect meaningful object regions using a smaller number of proposals than other object proposals methods. Further, our framework can achieve reliable object detection and classification results in maritime environments.

Jonne Poikonen

Papers

Streak detection and analysis pipeline for space-debris optical images

Towards Understanding the Formation of Uniform Local Binary Patterns

An Efficient Multi-sensor Fusion Approach for Object Detection in Maritime Environments

MIPA4k: Mixed-Mode Cellular Processor Array

Object Detection Based on Multi-sensor Proposal Fusion in Maritime Environment