4,698 research outputs found
Navigation without localisation: reliable teach and repeat based on the convergence theorem
We present a novel concept for teach-and-repeat visual navigation. The
proposed concept is based on a mathematical model, which indicates that in
teach-and-repeat navigation scenarios, mobile robots do not need to perform
explicit localisation. Rather than that, a mobile robot which repeats a
previously taught path can simply `replay' the learned velocities, while using
its camera information only to correct its heading relative to the intended
path. To support our claim, we establish a position error model of a robot,
which traverses a taught path by only correcting its heading. Then, we outline
a mathematical proof which shows that this position error does not diverge over
time. Based on the insights from the model, we present a simple monocular
teach-and-repeat navigation method. The method is computationally efficient, it
does not require camera calibration, and it can learn and autonomously traverse
arbitrarily-shaped paths. In a series of experiments, we demonstrate that the
method can reliably guide mobile robots in realistic indoor and outdoor
conditions, and can cope with imperfect odometry, landmark deficiency,
illumination variations and naturally-occurring environment changes.
Furthermore, we provide the navigation system and the datasets gathered at
http://www.github.com/gestom/stroll_bearnav.Comment: The paper will be presented at IROS 2018 in Madri
Point Pair Feature based Object Detection for Random Bin Picking
Point pair features are a popular representation for free form 3D object
detection and pose estimation. In this paper, their performance in an
industrial random bin picking context is investigated. A new method to generate
representative synthetic datasets is proposed. This allows to investigate the
influence of a high degree of clutter and the presence of self similar
features, which are typical to our application. We provide an overview of
solutions proposed in literature and discuss their strengths and weaknesses. A
simple heuristic method to drastically reduce the computational complexity is
introduced, which results in improved robustness, speed and accuracy compared
to the naive approach
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
From Concept to Market: Surgical Robot Development
Surgical robotics and supporting technologies have really become a prime example of modern applied
information technology infiltrating our everyday lives. The development of these systems spans across
four decades, and only the last few years brought the market value and saw the rising customer base
imagined already by the early developers. This chapter guides through the historical development of the
most important systems, and provide references and lessons learnt for current engineers facing similar
challenges. A special emphasis is put on system validation, assessment and clearance, as the most
commonly cited barrier hindering the wider deployment of a system
Efficient calibration of four wheel industrial AGVs
In this paper, we propose a novel method for extrinsic and intrinsic automatic calibration of four wheel industrial Automated Guided Vehicles (AGVs) compliant with Ackermann and Dual Drive kinematics. For each kinematic model the algorithm estimates the trajectories measured by an on-board sensor and the expected ones given the state of the wheels. The estimation exploits the model equations derived in this work which constrain calibration parameters and measurements from wheel encoders and sensor odometry. The parameter values are computed through closed-form solutions of least-square estimation. The method has been implemented on Programmable Logic Controllers and tested on industrial AGVs. The developed procedure computes the parameters in about 10−15 minutes, a significant improvement compared with one hour or more required by manual AGV calibration. Experiments with AGVs of various sizes in a warehouse have assessed the accuracy and stability of the proposed approach. The position accuracy achieved by AGVs calibrated with the proposed method is higher than the one achieved by manual calibration
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