Fast, Accurate Thin-Structure Obstacle Detection for Autonomous Mobile Robots

Safety is paramount for mobile robotic platforms such as self-driving cars and unmanned aerial vehicles. This work is devoted to a task that is indispensable for safety yet was largely overlooked in the past -- detecting obstacles that are of very thin structures, such as wires, cables and tree branches. This is a challenging problem, as thin objects can be problematic for active sensors such as lidar and sonar and even for stereo cameras. In this work, we propose to use video sequences for thin obstacle detection. We represent obstacles with edges in the video frames, and reconstruct them in 3D using efficient edge-based visual odometry techniques. We provide both a monocular camera solution and a stereo camera solution. The former incorporates Inertial Measurement Unit (IMU) data to solve scale ambiguity, while the latter enjoys a novel, purely vision-based solution. Experiments demonstrated that the proposed methods are fast and able to detect thin obstacles robustly and accurately under various conditions.Comment: Appeared at IEEE CVPR 2017 Workshop on Embedded Visio

High-Precision Localization Using Ground Texture

Location-aware applications play an increasingly critical role in everyday life. However, satellite-based localization (e.g., GPS) has limited accuracy and can be unusable in dense urban areas and indoors. We introduce an image-based global localization system that is accurate to a few millimeters and performs reliable localization both indoors and outside. The key idea is to capture and index distinctive local keypoints in ground textures. This is based on the observation that ground textures including wood, carpet, tile, concrete, and asphalt may look random and homogeneous, but all contain cracks, scratches, or unique arrangements of fibers. These imperfections are persistent, and can serve as local features. Our system incorporates a downward-facing camera to capture the fine texture of the ground, together with an image processing pipeline that locates the captured texture patch in a compact database constructed offline. We demonstrate the capability of our system to robustly, accurately, and quickly locate test images on various types of outdoor and indoor ground surfaces

ASSESSING THE EFFECTIVENESS OF A COMBAT UGV SWARM IN URBAN OPERATIONS

Due to its complexity, an urban area is a challenging multi-dimensional environment for ground warfare. Recent technological advancements have enabled militaries to utilize different-size unmanned ground vehicles (UGV) to support a variety of missions. This thesis presents guidance algorithms for a search and kill mission developed for some generic UGV swarms, which may be an attractive application, particularly for smaller UGVs operating in an urban environment. Through a series of computer simulations, the research evaluates the feasibility and effectiveness of the algorithms in executing such a mission in indoor and outdoor urban environments. The developed simulation allows varying many parameters, thus achieving closeness to the real-world situation when different environments, platforms, sensors, and weapons are used. Computer simulations presented in this paper may also assist military leaders in choosing key mission parameters to maximize the outcome of potential future engagements.http://archive.org/details/assessingtheeffe1094560354Outstanding ThesisArmy, SingaporeApproved for public release; distribution is unlimited