3D Visual Perception for Self-Driving Cars using a Multi-Camera System: Calibration, Mapping, Localization, and Obstacle Detection

Cameras are a crucial exteroceptive sensor for self-driving cars as they are low-cost and small, provide appearance information about the environment, and work in various weather conditions. They can be used for multiple purposes such as visual navigation and obstacle detection. We can use a surround multi-camera system to cover the full 360-degree field-of-view around the car. In this way, we avoid blind spots which can otherwise lead to accidents. To minimize the number of cameras needed for surround perception, we utilize fisheye cameras. Consequently, standard vision pipelines for 3D mapping, visual localization, obstacle detection, etc. need to be adapted to take full advantage of the availability of multiple cameras rather than treat each camera individually. In addition, processing of fisheye images has to be supported. In this paper, we describe the camera calibration and subsequent processing pipeline for multi-fisheye-camera systems developed as part of the V-Charge project. This project seeks to enable automated valet parking for self-driving cars. Our pipeline is able to precisely calibrate multi-camera systems, build sparse 3D maps for visual navigation, visually localize the car with respect to these maps, generate accurate dense maps, as well as detect obstacles based on real-time depth map extraction

Fast LIDAR-based Road Detection Using Fully Convolutional Neural Networks

In this work, a deep learning approach has been developed to carry out road detection using only LIDAR data. Starting from an unstructured point cloud, top-view images encoding several basic statistics such as mean elevation and density are generated. By considering a top-view representation, road detection is reduced to a single-scale problem that can be addressed with a simple and fast fully convolutional neural network (FCN). The FCN is specifically designed for the task of pixel-wise semantic segmentation by combining a large receptive field with high-resolution feature maps. The proposed system achieved excellent performance and it is among the top-performing algorithms on the KITTI road benchmark. Its fast inference makes it particularly suitable for real-time applications

AMTV headway sensor and safety design

A headway sensing system for an automated mixed traffic vehicle (AMTV) employing an array of optical proximity sensor elements is described, and its performance is presented in terms of object detection profiles. The problem of sensing in turns is explored experimentally and requirements for future turn sensors are discussed. A recommended headway sensor configuration, employing multiple source elements in the focal plane of one lens operating together with a similar detector unit, is described. Alternative concepts including laser radar, ultrasonic sensing, imaging techniques, and radar are compared to the present proximity sensor approach. Design concepts for an AMTV body which will minimize the probability of injury to pedestrians or passengers in the event of a collision are presented

Improving construction materials management practices in construction sites

Construction Materials Management is a vital function for improving productivity in construction projects. Poor materials management can often affect the overall construction time, quality and budget. Currently, the construction material management practice in Somalia is believed to be poorly performed. Lack of standardized construction materials management system is one of the key issues facing by the building industry in Mogadishu-Somalia. The aim of this study was to investigate the current practices of material management at construction sites in Mogadishu-Somalia. A questionnaire survey study design was used to explore construction materials management practices. Fifty questionnaires were distributed to project managers, project engineers, site engineers, engineer, and foreman, and they were received and analysed. The following data analysis techniques were used: descriptive statistics were conducted to report sample characteristics, reliability and validity analyses were performed to confirm robustness of the instrument, graphical presentation such as bar charts were developed, and finally Average Mean Index Scale were constructed. The study results reveals that, 46.7% of respondent’s organization obtain materials for sites without site requisition by site engineer provisions, while 28.9% of respondent’s organization procure materials for sites with site requisition by project manager provisions and 13.3% of respondent’s organization procure materials for site by engineer. The results indicated that currently there is no standardized and computerized construction materials management system applied in Somalia. The researcher concluded that all contracting companies are interested in using some techniques of managing construction materials such as creating and updating database for materials categories from local and international suppliers. Finally, researcher recommends to use computerized construction materials management systems to reduce effort and time, and to achieve more accurate results

Competing in the RoboCup Rescue Robot League

RoboCup Rescue is an international competition in which robots compete to find disaster victims in a simulated earthquake environment. It features both a Rescue Simulation League (RSL) which is entirely computer simulated, and a Rescue Robot League (RRL) with real robots and a test arena. This paper will describe the experience gained sending an undergraduate team to compete in the Rescue Robot League at the RoboCup German Open in 2008 and 2009. The design of the test arena and the rules of the competition will be outlined; as will the approaches taken by different teams; and the competition results