2,784 research outputs found
Using a Deep Learning Model on Images to Obtain a 2D Laser People Detector for a Mobile Robot
Recent improvements in deep learning techniques applied to images allow the detection of people with a high success rate. However,
other types of sensors, such as laser rangefinders, are still useful due to their wide field of vision and their ability to operate
in different environments and lighting conditions. In this work we use an interesting computational intelligence technique such
as the deep learning method to detect people in images taken by a mobile robot. The masks of the people in the images are used
to automatically label a set of samples formed by 2D laser range data that will allow us to detect the legs of people present in the
scene. The samples are geometric characteristics of the clusters built from the laser data. The machine learning algorithms are
used to learn a classifier that is capable of detecting people from only 2D laser range data. Our people detector is compared to
a state-of-the-art classifier. Our proposal achieves a higher value of F1 in the test set using an unbalanced dataset. To improve
accuracy, the final classifier has been generated from a balanced training set. This final classifier has also been evaluated using
a test set in which we have obtained very high accuracy values in each class. The contribution of this work is 2-fold. On the one
hand, our proposal performs an automatic labeling of the samples so that the dataset can be collected under real operating conditions.
On the other hand, the robot can detect people in a wider field of view than if we only used a camera, and in this way
can help build more robust behaviors.This work has been supported by the Spanish Government TIN2016-
76515-R Grant, supported with Feder funds
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
Deep Detection of People and their Mobility Aids for a Hospital Robot
Robots operating in populated environments encounter many different types of
people, some of whom might have an advanced need for cautious interaction,
because of physical impairments or their advanced age. Robots therefore need to
recognize such advanced demands to provide appropriate assistance, guidance or
other forms of support. In this paper, we propose a depth-based perception
pipeline that estimates the position and velocity of people in the environment
and categorizes them according to the mobility aids they use: pedestrian,
person in wheelchair, person in a wheelchair with a person pushing them, person
with crutches and person using a walker. We present a fast region proposal
method that feeds a Region-based Convolutional Network (Fast R-CNN). With this,
we speed up the object detection process by a factor of seven compared to a
dense sliding window approach. We furthermore propose a probabilistic position,
velocity and class estimator to smooth the CNN's detections and account for
occlusions and misclassifications. In addition, we introduce a new hospital
dataset with over 17,000 annotated RGB-D images. Extensive experiments confirm
that our pipeline successfully keeps track of people and their mobility aids,
even in challenging situations with multiple people from different categories
and frequent occlusions. Videos of our experiments and the dataset are
available at http://www2.informatik.uni-freiburg.de/~kollmitz/MobilityAidsComment: 7 pages, ECMR 2017, dataset and videos:
http://www2.informatik.uni-freiburg.de/~kollmitz/MobilityAids
Detecting and tracking using 2D laser range finders and deep learning
Detecting and tracking people using 2D laser rangefinders (LRFs) is challenging due to the features of the human leg motion, high levels of self-occlusion and the existence of objects which are similar to the human legs. Previous approaches use datasets that are manually labelled with support of images of the scenes. We propose a system with a calibrated monocular camera and 2D LRF mounted on a mobile robot in order to generate a dataset of leg patterns through automatic labelling which is valid to achieve a robust and efficient 2D LRF-based people detector and tracker. First, both images and 2D laser data are recorded during the robot navigation in indoor environments. Second, the people detection boxes and keypoints obtained by a deep learning-based object detector are used to locate both people and their legs on the images. The coordinates frame of 2D laser is extrinsically calibrated to the camera coordinates allowing our system to automatically label the leg instances. The automatically labelled dataset is then used to achieve a leg detector by machine learning techniques. To validate the proposal, the leg detector is used to develop a Kalman filter-based people detection and tracking algorithm which is experimentally assessed. The experimentation shows that the proposed system overcomes the Angus Leigh’s detector and tracker which is considered the state of the art on 2D LRF-based people detector and tracker.This work was supported under Grant PID2019-104818RB-I00 funded by MCIN/AEI/10.13039/501100011033 and by ‘‘European Regional Development Fund (ERDF) A way of making Europe’’.Funding for open access charge: Universidad de Granada / CBUA
RUR53: an Unmanned Ground Vehicle for Navigation, Recognition and Manipulation
This paper proposes RUR53: an Unmanned Ground Vehicle able to autonomously
navigate through, identify, and reach areas of interest; and there recognize,
localize, and manipulate work tools to perform complex manipulation tasks. The
proposed contribution includes a modular software architecture where each
module solves specific sub-tasks and that can be easily enlarged to satisfy new
requirements. Included indoor and outdoor tests demonstrate the capability of
the proposed system to autonomously detect a target object (a panel) and
precisely dock in front of it while avoiding obstacles. They show it can
autonomously recognize and manipulate target work tools (i.e., wrenches and
valve stems) to accomplish complex tasks (i.e., use a wrench to rotate a valve
stem). A specific case study is described where the proposed modular
architecture lets easy switch to a semi-teleoperated mode. The paper
exhaustively describes description of both the hardware and software setup of
RUR53, its performance when tests at the 2017 Mohamed Bin Zayed International
Robotics Challenge, and the lessons we learned when participating at this
competition, where we ranked third in the Gran Challenge in collaboration with
the Czech Technical University in Prague, the University of Pennsylvania, and
the University of Lincoln (UK).Comment: This article has been accepted for publication in Advanced Robotics,
published by Taylor & Franci
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SLAM in Dynamic Environments: A Deep Learning Approach for Moving Object Tracking Using ML-RANSAC Algorithm
The important problem of Simultaneous Localization and Mapping (SLAM) in dynamic environments is less studied than the counterpart problem in static settings. In this paper, we present a solution for the feature-based SLAM problem in dynamic environments. We propose an algorithm that integrates SLAM with multi-target tracking (SLAMMTT) using a robust feature-tracking algorithm for dynamic environments. A novel implementation of RANdomSAmple Consensus (RANSAC) method referred to as multilevel-RANSAC (ML-RANSAC) within the Extended Kalman Filter (EKF) framework is applied for multi-target tracking (MTT). We also apply machine learning to detect features from the input data and to distinguish moving from stationary objects. The data stream from LIDAR and vision sensors are fused in real-time to detect objects and depth information. A practical experiment is designed to verify the performance of the algorithm in a dynamic environment. The unique feature of this algorithm is its ability to maintain tracking of features even when the observations are intermittent whereby many reported algorithms fail in such situations. Experimental validation indicates that the algorithm is able to perform consistent estimates in a fast and robust manner suggesting its feasibility for real-time applications
From Perception to Navigation in Environments with Persons: An Indoor Evaluation of the State of the Art
Research in the field of social robotics is allowing service robots to operate in environments with people. In the aim of realizing the vision of humans and robots coexisting in the same environment, several solutions have been proposed to (1) perceive persons and objects in the immediate environment; (2) predict the movements of humans; as well as (3) plan the navigation in agreement with socially accepted rules. In this work, we discuss the different aspects related to social navigation in the context of our experience in an indoor environment. We describe state-of-the-art approaches and experiment with existing methods to analyze their performance in practice. From this study, we gather first-hand insights into the limitations of current solutions and identify possible research directions to address the open challenges. In particular, this paper focuses on topics related to perception at the hardware and application levels, including 2D and 3D sensors, geometric and mainly semantic mapping, the prediction of people trajectories (physics-, pattern- and planning-based), and social navigation (reactive and predictive) in indoor environments
A multi-modal person perception framework for socially interactive mobile service robots
In order to meet the increasing demands of mobile service robot applications, a dedicated perception module is an essential requirement for the interaction with users in real-world scenarios. In particular, multi sensor fusion and human re-identification are recognized as active research fronts. Through this paper we contribute to the topic and present a modular detection and tracking system that models position and additional properties of persons in the surroundings of a mobile robot. The proposed system introduces a probability-based data association method that besides the position can incorporate face and color-based appearance features in order to realize a re-identification of persons when tracking gets interrupted. The system combines the results of various state-of-the-art image-based detection systems for person recognition, person identification and attribute estimation. This allows a stable estimate of a mobile robot’s user, even in complex, cluttered environments with long-lasting occlusions. In our benchmark, we introduce a new measure for tracking consistency and show the improvements when face and appearance-based re-identification are combined. The tracking system was applied in a real world application with a mobile rehabilitation assistant robot in a public hospital. The estimated states of persons are used for the user-centered navigation behaviors, e.g., guiding or approaching a person, but also for realizing a socially acceptable navigation in public environments
A Cost-Effective Person-Following System for Assistive Unmanned Vehicles with Deep Learning at the Edge
The vital statistics of the last century highlight a sharp increment of the
average age of the world population with a consequent growth of the number of
older people. Service robotics applications have the potentiality to provide
systems and tools to support the autonomous and self-sufficient older adults in
their houses in everyday life, thereby avoiding the task of monitoring them
with third parties. In this context, we propose a cost-effective modular
solution to detect and follow a person in an indoor, domestic environment. We
exploited the latest advancements in deep learning optimization techniques, and
we compared different neural network accelerators to provide a robust and
flexible person-following system at the edge. Our proposed cost-effective and
power-efficient solution is fully-integrable with pre-existing navigation
stacks and creates the foundations for the development of fully-autonomous and
self-contained service robotics applications
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