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

Path planning for socially-aware humanoid robots

Designing efficient autonomous navigation systems for mobile robots involves consideration of the robotís environment while arriving at a systems architecture that trades off multiple constraints. We have architected a navigation framework for socially-aware autonomous robot navigation, using only the on-board computing resources. Our goal is to foster the development of several important service robotics applications using this platform. Our framework allows a robot to autonomously navigate in indoor environments while accounting for people (i.e., estimating the path of all individuals in the environment), respecting each individualís private space. In our design, we can leverage a wide number of sensors for navigation, including cameras, 2D and 3D scanners, and motion trackers. When designing our sensor system, we have considered that mobile robots have limited resources (i.e., power and computation) and that some sensors are costlier than others (e.g., cameras and 3D scanners stream data at high rates), requiring intensive computation to provide useful insight for real-time navigation. We tradeoff between accuracy, responsiveness, and power, and choose a Hokuyo UST-20LX 2D laser scanner for robot localization, obstacle detection and people tracking. We use an MPU-6050 for motion tracking. Our navigation framework features a low-power sensor system (< 5W) tailored for improved battery life in robotic applications while providing sufficient accuracy. We have completed a prototype for a Human Support Robot using the available onboard computing devices, requiring less than 60W to run. We estimate we can obtain similar performance, while reducing power by ~60%, utilizing low-power high-performance accelerator hardware and parallelized software.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tec

Pedestrian vision and collision avoidance behavior: investigation of the information process space of pedestrians using an eye tracker

This study investigates the Information Process Space (IPS) of pedestrians, which has been widely used in microscopic pedestrian movement simulation models. IPS is a conceptual framework to define the spatial extent within which all objects are considered as potential obstacles for each pedestrian when computing where to move next. The particular focus of our study was identifying the size and shape of IPS by examining observed gaze patterns of pedestrians. A series of experiments was conducted in a controlled laboratory environment, in which up to 4 participants walked on a platform at their natural speed. Their gaze patterns were recorded by a head-mounted eye tracker and walking paths by laser-range-scanner–based tracking systems at the frequency of 25Hz. Our findings are threefold: pedestrians pay much more attention to ground surfaces to detect immediate potential environmental hazards than fixating on obstacles; most of their fixations fall within a cone-shape area rather than a semicircle; and the attention paid to approaching pedestrians is not as high as that paid to static obstacles. These results led to an insight that the structure of IPS should be re-examined by researching directional characteristics of pedestrians’ vision

3D scanning of cultural heritage with consumer depth cameras

Three dimensional reconstruction of cultural heritage objects is an expensive and time-consuming process. Recent consumer real-time depth acquisition devices, like Microsoft Kinect, allow very fast and simple acquisition of 3D views. However 3D scanning with such devices is a challenging task due to the limited accuracy and reliability of the acquired data. This paper introduces a 3D reconstruction pipeline suited to use consumer depth cameras as hand-held scanners for cultural heritage objects. Several new contributions have been made to achieve this result. They include an ad-hoc filtering scheme that exploits the model of the error on the acquired data and a novel algorithm for the extraction of salient points exploiting both depth and color data. Then the salient points are used within a modified version of the ICP algorithm that exploits both geometry and color distances to precisely align the views even when geometry information is not sufficient to constrain the registration. The proposed method, although applicable to generic scenes, has been tuned to the acquisition of sculptures and in this connection its performance is rather interesting as the experimental results indicate

A machine learning approach to pedestrian detection for autonomous vehicles using High-Definition 3D Range Data

This article describes an automated sensor-based system to detect pedestrians in an autonomous vehicle application. Although the vehicle is equipped with a broad set of sensors, the article focuses on the processing of the information generated by a Velodyne HDL-64E LIDAR sensor. The cloud of points generated by the sensor (more than 1 million points per revolution) is processed to detect pedestrians, by selecting cubic shapes and applying machine vision and machine learning algorithms to the XY, XZ, and YZ projections of the points contained in the cube. The work relates an exhaustive analysis of the performance of three different machine learning algorithms: k-Nearest Neighbours (kNN), Naïve Bayes classifier (NBC), and Support Vector Machine (SVM). These algorithms have been trained with 1931 samples. The final performance of the method, measured a real traffic scenery, which contained 16 pedestrians and 469 samples of non-pedestrians, shows sensitivity (81.2%), accuracy (96.2%) and specificity (96.8%).This work was partially supported by ViSelTR (ref. TIN2012-39279) and cDrone (ref. TIN2013-45920-R) projects of the Spanish Government, and the “Research Programme for Groups of Scientific Excellence at Region of Murcia” of the Seneca Foundation (Agency for Science and Technology of the Region of Murcia—19895/GERM/15). 3D LIDAR has been funded by UPCA13-3E-1929 infrastructure projects of the Spanish Government. Diego Alonso wishes to thank the Spanish Ministerio de Educación, Cultura y Deporte, Subprograma Estatal de Movilidad, Plan Estatal de Investigación Científica y Técnica y de Innovación 2013–2016 for grant CAS14/00238