120 research outputs found
Height Change Feature Based Free Space Detection
In the context of autonomous forklifts, ensuring non-collision during travel,
pick, and place operations is crucial. To accomplish this, the forklift must be
able to detect and locate areas of free space and potential obstacles in its
environment. However, this is particularly challenging in highly dynamic
environments, such as factory sites and production halls, due to numerous
industrial trucks and workers moving throughout the area. In this paper, we
present a novel method for free space detection, which consists of the
following steps. We introduce a novel technique for surface normal estimation
relying on spherical projected LiDAR data. Subsequently, we employ the
estimated surface normals to detect free space. The presented method is a
heuristic approach that does not require labeling and can ensure real-time
application due to high processing speed. The effectiveness of the proposed
method is demonstrated through its application to a real-world dataset obtained
on a factory site both indoors and outdoors, and its evaluation on the Semantic
KITTI dataset [2]. We achieved a mean Intersection over Union (mIoU) score of
50.90 % on the benchmark dataset, with a processing speed of 105 Hz. In
addition, we evaluated our approach on our factory site dataset. Our method
achieved a mIoU score of 63.30 % at 54 H
Milestones in Autonomous Driving and Intelligent Vehicles Part II: Perception and Planning
Growing interest in autonomous driving (AD) and intelligent vehicles (IVs) is
fueled by their promise for enhanced safety, efficiency, and economic benefits.
While previous surveys have captured progress in this field, a comprehensive
and forward-looking summary is needed. Our work fills this gap through three
distinct articles. The first part, a "Survey of Surveys" (SoS), outlines the
history, surveys, ethics, and future directions of AD and IV technologies. The
second part, "Milestones in Autonomous Driving and Intelligent Vehicles Part I:
Control, Computing System Design, Communication, HD Map, Testing, and Human
Behaviors" delves into the development of control, computing system,
communication, HD map, testing, and human behaviors in IVs. This part, the
third part, reviews perception and planning in the context of IVs. Aiming to
provide a comprehensive overview of the latest advancements in AD and IVs, this
work caters to both newcomers and seasoned researchers. By integrating the SoS
and Part I, we offer unique insights and strive to serve as a bridge between
past achievements and future possibilities in this dynamic field.Comment: 17pages, 6figures. IEEE Transactions on Systems, Man, and
Cybernetics: System
Haptic robot-environment interaction for self-supervised learning in ground mobility
Dissertação para obtenção do Grau de Mestre em
Engenharia Eletrotécnica e de ComputadoresThis dissertation presents a system for haptic interaction and self-supervised learning mechanisms to ascertain navigation affordances from depth cues. A simple pan-tilt telescopic arm and a structured light sensor, both fitted to the robot’s body frame, provide the required haptic and depth sensory feedback. The system aims at incrementally develop the ability to assess the cost of navigating in natural environments. For this purpose the robot learns a mapping between the appearance
of objects, given sensory data provided by the sensor, and their bendability, perceived by the pan-tilt telescopic arm. The object descriptor, representing the object in memory and used for comparisons with other objects, is rich for a robust comparison and simple enough to allow for fast computations.
The output of the memory learning mechanism allied with the haptic interaction point evaluation prioritize interaction points to increase the confidence on the interaction and correctly identifying obstacles,
reducing the risk of the robot getting stuck or damaged. If the system concludes that the
object is traversable, the environment change detection system allows the robot to overcome it. A set of field trials show the ability of the robot to progressively learn which elements of environment are traversable
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
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