Pedestrian Detection using Triple Laser Range Finders

Pedestrian detection is one of the important features in autonomous ground vehicle (AGV). It ensures the capability for safety navigation in urban environment. Therefore, the detection accuracy became a crucial part which leads to implementation using Laser Range Finder (LRF) for better data representation. In this study, an improved laser configuration and fusion technique is introduced by implementation of triple LRFs in two layers with Pedestrian Data Analysis (PDA) to recognize multiple pedestrians. The PDA integrates various features from feature extraction process for all clusters and fusion of multiple layers for better recognition. The experiments were conducted in various occlusion scenarios such as intersection, closed-pedestrian and combine scenarios. The analysis of the laser fusion and PDA for all scenarios showed an improvement of detection where the pedestrians were represented by various detection categories which solve occlusion issues when low numberof laser data were obtained

Laser Reflection Intensity and Multi-Layered Laser Range Finders for People Detection

Abstract-Successful detection of people is a basic requirement for a robot to achieve symbiosis in people's daily life. Specifically, a mobile robot designed to follow people needs to keep track of people's position through time, for it defines the robot's position and trajectory. In this work we introduce the usage of reflection intensity data of Laser Range Finders (LRF) arranged in multiple layers for people detection. We use supervised learning to train strong classifiers including intensity-based features. Concretely, we propose a calibration method for laser intensity and introduce new intensity-based features for people detection which are combined with range-based features in a strong classifier using supervised learning. We provide experimental results to evaluate the effectiveness of these features. This work is an step towards of our main research project of developing a social autonomous mobile robot acting as member of a people group

Tracking of persons with camera-fusion technology

The idea of a robot tracking and following a person is not new. Different combinations of laser range finders and camera pairings have been used for research on this subject. In the last years stereoscopic systems have been developed to compensate shortcomings of laser range finders or ultra sonic sensor arrays in means of 3D recognition. When Microsoft began the distribution of the Microsoft Kinect in the year 2010 they released a comparatively cheap system, that combines depth measurement and a color view in one device. Though the system was intended as a new remote controlling system for games, tackling the market launch of motion sensing wireless controllers such as the Nintendo Wiimote and Sony Playstation 3 move some developers saw more in this technology. And so it did not take long until the first hacks for the Microsoft Kinect were published after the initial release. More and more people started to create own software, ranging from shadowpuppets [TW10] to remote controlling home cinema systems [Nar11]. Microsoft and PrimeSense soon recognized the potential and released free drivers and sdks for the use of the camera device with PCs. With Prime Sense publishing the drivers as open source a lot of possible uses came up for the Microsoft Kinect. Some companies used this event to enter the market of camera-fusion technology. The most comparable system to the Microsoft Kinect is the Xtion Pro Live by Asus. These devices merging the depth measurement and color view with computation on a deviceinternal system reveal new possibilities concerning the tracking of persons and enabling them to even give the robot commands using gestures. This paper shall inquire to what extend the Microsoft Kinect or the Asus Xtion Pro Live can be used as substitute for stereoscopic cameras/laser range finder systems in context of a tracking and control device for human robot interaction scenarios with person following applications for service robots

Behavior-based Control for Service Robots inspired by Human Motion Patterns : a Robotic Shopping Assistant

Es wurde, unter Verwendung menschenähnlicher Bewegungsmuster und eines verhaltensbasierten Ansatzes, eine Steuerung für mobile Serviceroboter entwickelt, die Aufgabenplanung, globale und lokale Navigation in dynamischen Umgebungen, sowie die gemeinsame Aufgabenausführung mit einem Benutzer umfasst. Das Verhaltensnetzwerk besteht aus Modulen mit voneinander unabhängigen Aufgaben. Das komplexe Gesamtverhalten des Systems ergibt sich durch die Vereinigung der Einzelverhalten (\u27Emergenz\u27)

Hybrid Control of a Segway Platform Developed in MRDS

A Segway RMP200 has been bought by Victoria University for the purpose of making an autonomous robot. The focus of this project was to create reusable services that use existing navigation algorithms to control the Segway within an indoor environment. A SICK LMS100 laser rangefinder was added to detect obstacles and allow localization of the Segway within a known map. A hybrid navigation algorithm consisting of an A* path planner with a dynamic window is used for motion planning and obstacle avoidance. The control system followed a Service Oriented Architecture implemented in Microsoft Robotics Studio using the C# .NET programming language. Four services were created during the project to interface with the SICK LMS100 scanner, control the Segway RMP200, implement the hybrid navigation algorithm and provide a graphic user interface for the system. Tests show that the Segway is able to navigate and maintain localisation within the operating environment by identifying and associating corner and door landmarks within the environment

Object Tracking

Object tracking consists in estimation of trajectory of moving objects in the sequence of images. Automation of the computer object tracking is a difficult task. Dynamics of multiple parameters changes representing features and motion of the objects, and temporary partial or full occlusion of the tracked objects have to be considered. This monograph presents the development of object tracking algorithms, methods and systems. Both, state of the art of object tracking methods and also the new trends in research are described in this book. Fourteen chapters are split into two sections. Section 1 presents new theoretical ideas whereas Section 2 presents real-life applications. Despite the variety of topics contained in this monograph it constitutes a consisted knowledge in the field of computer object tracking. The intention of editor was to follow up the very quick progress in the developing of methods as well as extension of the application

Mobile Robots Navigation

Mobile robots navigation includes different interrelated activities: (i) perception, as obtaining and interpreting sensory information; (ii) exploration, as the strategy that guides the robot to select the next direction to go; (iii) mapping, involving the construction of a spatial representation by using the sensory information perceived; (iv) localization, as the strategy to estimate the robot position within the spatial map; (v) path planning, as the strategy to find a path towards a goal location being optimal or not; and (vi) path execution, where motor actions are determined and adapted to environmental changes. The book addresses those activities by integrating results from the research work of several authors all over the world. Research cases are documented in 32 chapters organized within 7 categories next described