Robot Localization Using Visual Image Mapping

One critical step in providing the Air Force the capability to explore unknown environments is for an autonomous agent to be able to determine its location. The calculation of the robot\u27s pose is an optimization problem making use of the robot\u27s internal navigation sensors and data fusion of range sensor readings to find the most likely pose. This data fusion process requires the simultaneous generation of a map which the autonomous vehicle can then use to avoid obstacles, communicate with other agents in the same environment, and locate targets. Our solution entails mounting a Class 1 laser to an ERS-7 AIBO. The laser projects a horizontal line on obstacles in the AIBO camera\u27s field of view. Range readings are determined by capturing and processing multiple image frames, resolving the laser line to the horizon, and extract distance information to each obstacle. This range data is then used in conjunction with mapping a localization software to accurately navigate the AIBO

Methods for the automatic alignment of colour histograms

Colour provides important information in many image processing tasks such as object identification and tracking. Different images of the same object frequently yield different colour values due to undesired variations in lighting and the camera. In practice, controlling the source of these fluctuations is difficult, uneconomical or even impossible in a particular imaging environment. This thesis is concerned with the question of how to best align the corresponding clusters of colour histograms to reduce or remove the effect of these undesired variations. We introduce feature based histogram alignment (FBHA) algorithms that enable flexible alignment transformations to be applied. The FBHA approach has three steps, 1) feature detection in the colour histograms, 2) feature association and 3) feature alignment. We investigate the choices for these three steps on two colour databases : 1) a structured and labeled database of RGB imagery acquired under controlled camera, lighting and object variation and 2) grey-level video streams from an industrial inspection application. The design and acquisition of the RGB image and grey-level video databases are a key contribution of the thesis. The databases are used to quantitatively compare the FBHA approach against existing methodologies and show it to be effective. FBHA is intended to provide a generic method for aligning colour histograms, it only uses information from the histograms and therefore ignores spatial information in the image. Spatial information and other context sensitive cues are deliberately avoided to maintain the generic nature of the algorithm; by ignoring some of this important information we gain useful insights into the performance limits of a colour alignment algorithm that works from the colour histogram alone, this helps understand the limits of a generic approach to colour alignment

World Robot Challenge 2020 -- Partner Robot: A Data-Driven Approach for Room Tidying with Mobile Manipulator

Tidying up a household environment using a mobile manipulator poses various challenges in robotics, such as adaptation to large real-world environmental variations, and safe and robust deployment in the presence of humans.The Partner Robot Challenge in World Robot Challenge (WRC) 2020, a global competition held in September 2021, benchmarked tidying tasks in the real home environments, and importantly, tested for full system performances.For this challenge, we developed an entire household service robot system, which leverages a data-driven approach to adapt to numerous edge cases that occur during the execution, instead of classical manual pre-programmed solutions. In this paper, we describe the core ingredients of the proposed robot system, including visual recognition, object manipulation, and motion planning. Our robot system won the second prize, verifying the effectiveness and potential of data-driven robot systems for mobile manipulation in home environments

Omnidirectional Stereo Vision for Autonomous Vehicles

Environment perception with cameras is an important requirement for many applications for autonomous vehicles and robots. This work presents a stereoscopic omnidirectional camera system for autonomous vehicles which resolves the problem of a limited field of view and provides a 360° panoramic view of the environment. We present a new projection model for these cameras and show that the camera setup overcomes major drawbacks of traditional perspective cameras in many applications