1,022 research outputs found
Simultaneous maximum-likelihood calibration of odometry and sensor parameters
For a differential-drive mobile robot equipped with an on-board range sensor, there are six parameters to calibrate: three for the odometry (radii and distance between the wheels), and three for the pose of the sensor with respect to the robot frame. This paper describes a method for calibrating all six parameters at the same time, without the need for external sensors or devices. Moreover, it is not necessary to drive the robot along particular trajectories. The available data are the measures of the angular velocities of the wheels and the range sensor readings. The maximum-likelihood calibration solution is found in a closed form
Dynamic Motion Modelling for Legged Robots
An accurate motion model is an important component in modern-day robotic
systems, but building such a model for a complex system often requires an
appreciable amount of manual effort. In this paper we present a motion model
representation, the Dynamic Gaussian Mixture Model (DGMM), that alleviates the
need to manually design the form of a motion model, and provides a direct means
of incorporating auxiliary sensory data into the model. This representation and
its accompanying algorithms are validated experimentally using an 8-legged
kinematically complex robot, as well as a standard benchmark dataset. The
presented method not only learns the robot's motion model, but also improves
the model's accuracy by incorporating information about the terrain surrounding
the robot
Joint on-manifold self-calibration of odometry model and sensor extrinsics using pre-integration
Š 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.This paper describes a self-calibration procedure that jointly estimates the extrinsic parameters of an exteroceptive sensor able to observe ego-motion, and the intrinsic parameters of an odometry motion model, consisting of wheel radii and wheel separation. We use iterative nonlinear onmanifold optimization with a graphical representation of the state, and resort to an adaptation of the pre-integration theory, initially developed for the IMU motion sensor, to be applied to the differential drive motion model. For this, we describe the construction of a pre-integrated factor for the differential drive motion model, which includes the motion increment, its covariance, and a first-order approximation of its dependence with the calibration parameters. As the calibration parameters change at each solver iteration, this allows a posteriori factor correction without the need of re-integrating the motion data. We validate our proposal in simulations and on a real robot and show the convergence of the calibration towards the true values of the parameters. It is then tested online in simulation and is shown to accommodate to variations in the calibration parameters when the vehicle is subject to physical changes such as loading and unloading a freight.Peer ReviewedPostprint (author's final draft
Tahap penguasaan, sikap dan minat pelajar Kolej Kemahiran Tinggi MARA terhadap mata pelajaran Bahasa Inggeris
Kajian ini dilakukan untuk mengenal pasti tahap penguasaan, sikap dan minat pelajar
Kolej Kemahiran Tinggi Mara Sri Gading terhadap Bahasa Inggeris. Kajian yang
dijalankan ini berbentuk deskriptif atau lebih dikenali sebagai kaedah tinjauan. Seramai
325 orang pelajar Diploma in Construction Technology dari Kolej Kemahiran Tinggi
Mara di daerah Batu Pahat telah dipilih sebagai sampel dalam kajian ini. Data yang
diperoleh melalui instrument soal selidik telah dianalisis untuk mendapatkan
pengukuran min, sisihan piawai, dan Pekali Korelasi Pearson untuk melihat hubungan
hasil dapatan data. Manakala, frekuensi dan peratusan digunakan bagi mengukur
penguasaan pelajar. Hasil dapatan kajian menunjukkan bahawa tahap penguasaan
Bahasa Inggeris pelajar adalah berada pada tahap sederhana manakala faktor utama yang
mempengaruhi penguasaan Bahasa Inggeris tersebut adalah minat diikuti oleh sikap.
Hasil dapatan menggunakan pekali Korelasi Pearson juga menunjukkan bahawa terdapat
hubungan yang signifikan antara sikap dengan penguasaan Bahasa Inggeris dan antara
minat dengan penguasaan Bahasa Inggeris. Kajian menunjukkan bahawa semakin positif
sikap dan minat pelajar terhadap pengajaran dan pembelajaran Bahasa Inggeris semakin
tinggi pencapaian mereka. Hasil daripada kajian ini diharapkan dapat membantu pelajar
dalam meningkatkan penguasaan Bahasa Inggeris dengan memupuk sikap positif dalam
diri serta meningkatkan minat mereka terhadap Bahasa Inggeris dengan lebih baik. Oleh
itu, diharap kajian ini dapat memberi panduan kepada pihak-pihak yang terlibat dalam
membuat kajian yang akan datang
Monocular navigation for long-term autonomy
We present a reliable and robust monocular navigation system for an autonomous vehicle.
The proposed method is computationally efficient, needs off-the-shelf equipment only and does not require any additional infrastructure like radio beacons or GPS.
Contrary to traditional localization algorithms, which use advanced mathematical methods to determine vehicle position, our method uses a more practical approach.
In our case, an image-feature-based monocular vision technique determines only the heading of the vehicle while the vehicle's odometry is used to estimate the distance traveled.
We present a mathematical proof and experimental evidence indicating that the localization error of a robot guided by this principle is bound.
The experiments demonstrate that the method can cope with variable illumination, lighting deficiency and both short- and long-term environment changes.
This makes the method especially suitable for deployment in scenarios which require long-term autonomous operation
Probabilistic RGB-D Odometry based on Points, Lines and Planes Under Depth Uncertainty
This work proposes a robust visual odometry method for structured
environments that combines point features with line and plane segments,
extracted through an RGB-D camera. Noisy depth maps are processed by a
probabilistic depth fusion framework based on Mixtures of Gaussians to denoise
and derive the depth uncertainty, which is then propagated throughout the
visual odometry pipeline. Probabilistic 3D plane and line fitting solutions are
used to model the uncertainties of the feature parameters and pose is estimated
by combining the three types of primitives based on their uncertainties.
Performance evaluation on RGB-D sequences collected in this work and two public
RGB-D datasets: TUM and ICL-NUIM show the benefit of using the proposed depth
fusion framework and combining the three feature-types, particularly in scenes
with low-textured surfaces, dynamic objects and missing depth measurements.Comment: Major update: more results, depth filter released as opensource, 34
page
A Robust Localization System for Inspection Robots in Sewer Networks â
Sewers represent a very important infrastructure of cities whose state should be monitored
periodically. However, the length of such infrastructure prevents sensor networks from being
applicable. In this paper, we present a mobile platform (SIAR) designed to inspect the sewer network.
It is capable of sensing gas concentrations and detecting failures in the network such as cracks and
holes in the floor and walls or zones were the water is not flowing. These alarms should be precisely
geo-localized to allow the operators performing the required correcting measures. To this end, this
paper presents a robust localization system for global pose estimation on sewers. It makes use of prior
information of the sewer network, including its topology, the different cross sections traversed and
the position of some elements such as manholes. The system is based on a Monte Carlo Localization
system that fuses wheel and RGB-D odometry for the prediction stage. The update step takes into
account the sewer network topology for discarding wrong hypotheses. Additionally, the localization
is further refined with novel updating steps proposed in this paper which are activated whenever
a discrete element in the sewer network is detected or the relative orientation of the robot over the
sewer gallery could be estimated. Each part of the system has been validated with real data obtained
from the sewers of Barcelona. The whole system is able to obtain median localization errors in the
order of one meter in all cases. Finally, the paper also includes comparisons with state-of-the-art
Simultaneous Localization and Mapping (SLAM) systems that demonstrate the convenience of the
approach.UniĂłn Europea ECHORD ++ 601116Ministerio de Ciencia, InnovaciĂłn y Universidades de EspaĂąa RTI2018-100847-B-C2
Systematic Odometry Error Evaluation and Correction in a Human-Sized Three-Wheeled Omnidirectional Mobile Robot Using Flower-Shaped Calibration Trajectories
Odometry is a simple and practical method that provides a periodic real-time estimation of
the relative displacement of a mobile robot based on the measurement of the angular rotational speed
of its wheels. The main disadvantage of odometry is its unbounded accumulation of errors, a factor
that reduces the accuracy of the estimation of the absolute position and orientation of a mobile robot.
This paper proposes a general procedure to evaluate and correct the systematic odometry errors of a
human-sized three-wheeled omnidirectional mobile robot designed as a versatile personal assistant
tool. The correction procedure is based on the definition of 36 individual calibration trajectories
which together depict a flower-shaped figure, on the measurement of the odometry and ground
truth trajectory of each calibration trajectory, and on the application of several strategies to iteratively
adjust the effective value of the kinematic parameters of the mobile robot in order to match the
estimated final position from these two trajectories. The results have shown an average improvement
of 82.14% in the estimation of the final position and orientation of the mobile robot. Therefore, these
results can be used for odometry calibration during the manufacturing of human-sized three-wheeled
omnidirectional mobile robots
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