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

Perception for detection and grasping

The final publication is available at link.springer.comThis research presents a methodology for the detection of the crawler used in the project AEROARMS. The approach consisted on using a two-step progressive strategy, going from rough detection and tracking, for approximation maneuvers, to an accurate positioning step based on fiducial markers. Two different methods are explained for the first step, one using efficient image segmentation approach; and the second one using Deep Learning techniques to detect the center of the crawler. The fiducial markers are used for precise localization of the crawler in a similar way as explained in earlier chapters. The methods can run in real-time.Peer ReviewedPostprint (author's final draft

A Neural Model of How the Brain Computes Heading from Optic Flow in Realistic Scenes

Animals avoid obstacles and approach goals in novel cluttered environments using visual information, notably optic flow, to compute heading, or direction of travel, with respect to objects in the environment. We present a neural model of how heading is computed that describes interactions among neurons in several visual areas of the primate magnocellular pathway, from retina through V1, MT+, and MSTd. The model produces outputs which are qualitatively and quantitatively similar to human heading estimation data in response to complex natural scenes. The model estimates heading to within 1.5° in random dot or photo-realistically rendered scenes and within 3° in video streams from driving in real-world environments. Simulated rotations of less than 1 degree per second do not affect model performance, but faster simulated rotation rates deteriorate performance, as in humans. The model is part of a larger navigational system that identifies and tracks objects while navigating in cluttered environments.National Science Foundation (SBE-0354378, BCS-0235398); Office of Naval Research (N00014-01-1-0624); National-Geospatial Intelligence Agency (NMA201-01-1-2016

Investigation on the mobile robot navigation in an unknown environment

Mobile robots could be used to search, find, and relocate objects in many types of manufacturing operations and environments. In this scenario, the target objects might reside with equal probability at any location in the environment and, therefore, the robot must navigate and search the whole area autonomously, and be equipped with specific sensors to detect objects. Novel challenges exist in developing a control system, which helps a mobile robot achieve such tasks, including constructing enhanced systems for navigation, and vision-based object recognition. The latter is important for undertaking the exploration task that requires an optimal object recognition technique. In this thesis, these challenges, for an indoor environment, were divided into three sub-problems. In the first, the navigation task involved discovering an appropriate exploration path for the entire environment, with minimal sensing requirements. The Bug algorithm strategies were adapted for modelling the environment and implementing the exploration path. The second was a visual-search process, which consisted of employing appropriate image-processing techniques, and choosing a suitable viewpoint field for the camera. This study placed more emphasis on colour segmentation, template matching and Speeded-Up Robust Features (SURF) for object detection. The third problem was the relocating process, which involved using a robot’s gripper to grasp the detected, desired object and then move it to the assigned, final location. This also included approaching both the target and the delivery site, using a visual tracking technique. All codes were developed using C++ and C programming, and some libraries that included OpenCV and OpenSURF were utilized for image processing. Each control system function was tested both separately, and then in combination as a whole control program. The system performance was evaluated using two types of mobile robots: legged and wheeled. In this study, it was necessary to develop a wheeled search robot with a high performance processor. The experimental results demonstrated that the methodology used for the search robots was highly efficient provided the processor was adequate. It was concluded that it is possible to implement a navigation system within a minimum number of sensors if they are located and used effectively on the robot’s body. The main challenge within a visual-search process is that the environmental conditions are difficult to control, because the search robot executes its tasks in dynamic environments. The additional challenges of scaling these small robots up to useful industrial capabilities were also explored