DROW: Real-Time Deep Learning based Wheelchair Detection in 2D Range Data

We introduce the DROW detector, a deep learning based detector for 2D range data. Laser scanners are lighting invariant, provide accurate range data, and typically cover a large field of view, making them interesting sensors for robotics applications. So far, research on detection in laser range data has been dominated by hand-crafted features and boosted classifiers, potentially losing performance due to suboptimal design choices. We propose a Convolutional Neural Network (CNN) based detector for this task. We show how to effectively apply CNNs for detection in 2D range data, and propose a depth preprocessing step and voting scheme that significantly improve CNN performance. We demonstrate our approach on wheelchairs and walkers, obtaining state of the art detection results. Apart from the training data, none of our design choices limits the detector to these two classes, though. We provide a ROS node for our detector and release our dataset containing 464k laser scans, out of which 24k were annotated.Comment: Lucas Beyer and Alexander Hermans contributed equall

A framework for roadmap-based navigation and sector-based localization of mobile robots

Personal robotics applications require autonomous mobile robot navigation methods that are safe, robust, and inexpensive. Two requirements for autonomous use of robots for such applications are an automatic motion planner to select paths and a robust way of ensuring that the robot can follow the selected path given the unavoidable odometer and control errors that must be dealt with for any inexpensive robot. Additional difficulties are faced when there is more than one robot involved. In this dissertation, we describe a new roadmapbased method for mobile robot navigation. It is suitable for partially known indoor environments and requires only inexpensive range sensors. The navigator selects paths from the roadmap and designates localization points on those paths. In particular, the navigator selects feasible paths that are sensitive to the needs of the application (e.g., no sharp turns) and of the localization algorithm (e.g., within sensing range of two features). We present a new sectorbased localizer that is robust in the presence of sensor limitations and unknown obstacles while still maintaining computational efficiency. We extend our approach to teams of robots focusing on quickly sensing ranges from all robots while avoiding sensor crosstalk, and reducing the pose uncertainties of all robots while using a minimal number of sensing rounds. We present experimental results for mobile robots and describe a webbased route planner for the Texas A&M campus that utilizes our navigator

Detecting and avoiding frontal obstacles from monocular camera for micro unmanned aerial vehicles

In literature, several approaches are trying to make the UAVs fly autonomously i.e., by extracting perspective cues such as straight lines. However, it is only available in well-defined human made environments, in addition to many other cues which require enough texture information. Our main target is to detect and avoid frontal obstacles from a monocular camera using a quad rotor Ar.Drone 2 by exploiting optical flow as a motion parallax, the drone is permitted to fly at a speed of 1 m/s and an altitude ranging from 1 to 4 meters above the ground level. In general, detecting and avoiding frontal obstacle is a quite challenging problem because optical flow has some limitation which should be taken into account i.e. lighting conditions and aperture problem

Vision Science and Technology at NASA: Results of a Workshop

A broad review is given of vision science and technology within NASA. The subject is defined and its applications in both NASA and the nation at large are noted. A survey of current NASA efforts is given, noting strengths and weaknesses of the NASA program

Geodesic Paths On 3D Surfaces: Survey and Open Problems

This survey gives a brief overview of theoretically and practically relevant algorithms to compute geodesic paths and distances on three-dimensional surfaces. The survey focuses on polyhedral three-dimensional surfaces