2,952 research outputs found
Unevenness Point Descriptor for Terrain Analysis in Mobile Robot Applications
In recent years, the use of imaging sensors that produce a three-dimensional representation of the environment has become an efficient solution to increase the degree of perception of autonomous mobile robots. Accurate and dense 3D point clouds can be generated from traditional stereo systems and laser scanners or from the new generation of RGB-D cameras, representing a versatile, reliable and cost-effective solution that is rapidly gaining interest within the robotics community. For autonomous mobile robots, it is critical to assess the traversability of the surrounding environment, especially when driving across natural terrain. In this paper, a novel approach to detect traversable and non-traversable regions of the environment from a depth image is presented that could enhance mobility and safety through integration with localization, control and planning methods. The proposed algorithm is based on the analysis of the normal vector of a surface obtained through Principal Component Analysis and it leads to the definition of a novel, so defined, Unevenness Point Descriptor. Experimental results, obtained with vehicles operating in indoor and outdoor environments, are presented to validate this approach
Learning Ground Traversability from Simulations
Mobile ground robots operating on unstructured terrain must predict which
areas of the environment they are able to pass in order to plan feasible paths.
We address traversability estimation as a heightmap classification problem: we
build a convolutional neural network that, given an image representing the
heightmap of a terrain patch, predicts whether the robot will be able to
traverse such patch from left to right. The classifier is trained for a
specific robot model (wheeled, tracked, legged, snake-like) using simulation
data on procedurally generated training terrains; the trained classifier can be
applied to unseen large heightmaps to yield oriented traversability maps, and
then plan traversable paths. We extensively evaluate the approach in simulation
on six real-world elevation datasets, and run a real-robot validation in one
indoor and one outdoor environment.Comment: Webpage: http://romarcg.xyz/traversability_estimation
Automatic Reconstruction of Textured 3D Models
Three dimensional modeling and visualization of environments is an increasingly important problem. This work addresses the problem of automatic 3D reconstruction and we present a system for unsupervised reconstruction of textured 3D models in the context of modeling indoor environments. We present solutions to all aspects of the modeling process and an integrated system for the automatic creation of large scale 3D models
Past, Present, and Future of Simultaneous Localization And Mapping: Towards the Robust-Perception Age
Simultaneous Localization and Mapping (SLAM)consists in the concurrent
construction of a model of the environment (the map), and the estimation of the
state of the robot moving within it. The SLAM community has made astonishing
progress over the last 30 years, enabling large-scale real-world applications,
and witnessing a steady transition of this technology to industry. We survey
the current state of SLAM. We start by presenting what is now the de-facto
standard formulation for SLAM. We then review related work, covering a broad
set of topics including robustness and scalability in long-term mapping, metric
and semantic representations for mapping, theoretical performance guarantees,
active SLAM and exploration, and other new frontiers. This paper simultaneously
serves as a position paper and tutorial to those who are users of SLAM. By
looking at the published research with a critical eye, we delineate open
challenges and new research issues, that still deserve careful scientific
investigation. The paper also contains the authors' take on two questions that
often animate discussions during robotics conferences: Do robots need SLAM? and
Is SLAM solved
Autonomous navigation for guide following in crowded indoor environments
The requirements for assisted living are rapidly changing as the number of elderly
patients over the age of 60 continues to increase. This rise places a high level of stress on
nurse practitioners who must care for more patients than they are capable. As this trend is
expected to continue, new technology will be required to help care for patients. Mobile
robots present an opportunity to help alleviate the stress on nurse practitioners by
monitoring and performing remedial tasks for elderly patients. In order to produce
mobile robots with the ability to perform these tasks, however, many challenges must be
overcome.
The hospital environment requires a high level of safety to prevent patient injury. Any
facility that uses mobile robots, therefore, must be able to ensure that no harm will come
to patients whilst in a care environment. This requires the robot to build a high level of
understanding about the environment and the people with close proximity to the robot.
Hitherto, most mobile robots have used vision-based sensors or 2D laser range finders.
3D time-of-flight sensors have recently been introduced and provide dense 3D point
clouds of the environment at real-time frame rates. This provides mobile robots with
previously unavailable dense information in real-time. I investigate the use of time-of-flight
cameras for mobile robot navigation in crowded environments in this thesis. A
unified framework to allow the robot to follow a guide through an indoor environment
safely and efficiently is presented. Each component of the framework is analyzed in
detail, with real-world scenarios illustrating its practical use.
Time-of-flight cameras are relatively new sensors and, therefore, have inherent problems
that must be overcome to receive consistent and accurate data. I propose a novel and
practical probabilistic framework to overcome many of the inherent problems in this
thesis. The framework fuses multiple depth maps with color information forming a
reliable and consistent view of the world. In order for the robot to interact with the
environment, contextual information is required. To this end, I propose a region-growing
segmentation algorithm to group points based on surface characteristics, surface normal
and surface curvature. The segmentation process creates a distinct set of surfaces,
however, only a limited amount of contextual information is available to allow for
interaction. Therefore, a novel classifier is proposed using spherical harmonics to
differentiate people from all other objects.
The added ability to identify people allows the robot to find potential candidates to
follow. However, for safe navigation, the robot must continuously track all visible
objects to obtain positional and velocity information. A multi-object tracking system is
investigated to track visible objects reliably using multiple cues, shape and color. The
tracking system allows the robot to react to the dynamic nature of people by building an
estimate of the motion flow. This flow provides the robot with the necessary information
to determine where and at what speeds it is safe to drive. In addition, a novel search
strategy is proposed to allow the robot to recover a guide who has left the field-of-view.
To achieve this, a search map is constructed with areas of the environment ranked
according to how likely they are to reveal the guide’s true location. Then, the robot can
approach the most likely search area to recover the guide. Finally, all components
presented are joined to follow a guide through an indoor environment. The results
achieved demonstrate the efficacy of the proposed components
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