A modified model for the Lobula Giant Movement Detector and its FPGA implementation

The Lobula Giant Movement Detector (LGMD) is a wide-field visual neuron located in the Lobula layer of the Locust nervous system. The LGMD increases its firing rate in response to both the velocity of an approaching object and the proximity of this object. It has been found that it can respond to looming stimuli very quickly and trigger avoidance reactions. It has been successfully applied in visual collision avoidance systems for vehicles and robots. This paper introduces a modified neural model for LGMD that provides additional depth direction information for the movement. The proposed model retains the simplicity of the previous model by adding only a few new cells. It has been simplified and implemented on a Field Programmable Gate Array (FPGA), taking advantage of the inherent parallelism exhibited by the LGMD, and tested on real-time video streams. Experimental results demonstrate the effectiveness as a fast motion detector

A metrological characterization of the Kinect V2 time-of-flight camera

A metrological characterization process for time-of-flight (TOF) cameras is proposed in this paper and applied to the Microsoft Kinect V2. Based on the Guide to the Expression of Uncertainty in Measurement (GUM), the uncertainty of a three-dimensional (3D) scene reconstruction is analysed. In particular, the random and the systematic components of the uncertainty are evaluated for the single sensor pixel and for the complete depth camera. The manufacturer declares an uncertainty in the measurement of the central pixel of the sensor of about few millimetres (Kinect for Windows Features, 2015), which is considerably better than the first version of the Microsoft Kinect (Chow et al., 2012 [1]). This work points out that performances are highly influenced by measuring conditions and environmental parameters of the scene; actually the 3D point reconstruction uncertainty can vary from 1.5 to tens of millimetres

FEUPCAR 2.0 : Condução autónoma no Festival Nacional de Robótica

Tese de mestrado integrado. Engenharia Electrotécnica e de Computadores (Major Automação). Faculdade de Engenharia. Universidade do Porto. 201

Real-Time GPS-Alternative Navigation Using Commodity Hardware

Modern navigation systems can use the Global Positioning System (GPS) to accurately determine position with precision in some cases bordering on millimeters. Unfortunately, GPS technology is susceptible to jamming, interception, and unavailability indoors or underground. There are several navigation techniques that can be used to navigate during times of GPS unavailability, but there are very few that result in GPS-level precision. One method of achieving high precision navigation without GPS is to fuse data obtained from multiple sensors. This thesis explores the fusion of imaging and inertial sensors and implements them in a real-time system that mimics human navigation. In addition, programmable graphics processing unit technology is leveraged to perform stream-based image processing using a computer\u27s video card. The resulting system can perform complex mathematical computations in a fraction of the time those same operations would take on a CPU-based platform. The resulting system is an adaptable, portable, inexpensive and self-contained software and hardware platform, which paves the way for advances in autonomous navigation, mobile cartography, and artificial intelligence

VisLab and the Evolution of Vision-Based UGVs,

Thanks to the reduced costs of image acquisition devices and to the increasing computational power of current computer systems, Computer Vision has recently become a very popular method to sense the surrounding environment. This work presents a challenging application of machine vision to the automatic guidance of autonomous vehicles, discusses the key problems intrinsic to this field, and describes the solutions adopted in the development of different prototype vehicles worldwide. In the second part this paper focuses on the GOLD system, a stereo vision system developed at the University of Parma, Italy, for generic obstacle detection and lane localization, able to process images in real-time. GOLD was tested on the MOB-LAB experimental land vehicle for more than 3,000~km along extra-urban roads and freeways at speeds up to 80 km/h