Laser interferometry measurements based calibration and error propagation identification for pose estimation in mobile robots

Robotica ◽  
2013 ◽  
Vol 32 (1) ◽  
pp. 165-174 ◽  
Author(s):  
Paulo A. Jiménez ◽  
Bijan Shirinzadeh
Keyword(s):  
Mobile Robots ◽  
Pose Estimation ◽  
Error Propagation ◽  
Laser Interferometry ◽  
Error Variance ◽  
Calibration Method ◽  
Novel Method ◽  
Position And Orientation ◽  
Odometry Calibration ◽  
Two Parameter

SUMMARYA widely used method for pose estimation in mobile robots is odometry. Odometry allows the robot in real time to reconstruct its position and orientation from the wheels' encoder measurements. Given to its unbounded nature, odometry calculation accumulates errors with quadratic increase of error variance with traversed distance. This paper develops a novel method for odometry calibration and error propagation identification for mobile robots. The proposed method uses a laser-based interferometer to measure distance precisely. Two variants of the proposed calibration method are examined: the two-parameter model and the three-parameter model. Experimental results obtained using a Khepera 3 mobile robot showed that both methods significantly increase accuracy of the pose estimation, validating the effectiveness of the proposed calibration method.

scholarly journals Improved hybrid A* path planning method for spherical mobile robot based on pendulum

2021 ◽  
Vol 18 (1) ◽  
pp. 172988142199295
Author(s):  
Ziang Zhang ◽  
Yixu Wan ◽  
You Wang ◽  
Xiaoqing Guan ◽  
Wei Ren ◽  
...  
Keyword(s):  
Mobile Robots ◽  
Common Ground ◽  
Kinematic Model ◽  
Point Contact ◽  
Shell Shape ◽  
Spherical Robot ◽  
Trajectory Tracking Control ◽  
Novel Method ◽  
Turning Radius ◽  
Spherical Mobile Robot

This article proposes a modification of hybrid A* method used for navigation of spherical mobile robots with the ability of limited partial lateral movement driven by pendulum. For pendulum-driven spherical robots with nonzero minimal turning radius, our modification helps to find a feasible and achievable path, which can be followed in line with the low time cost. Because of spherical shell shape, the robot is point contact with the ground, showing different kinematic model compared with common ground mobile robots such as differential robot and wheeled car-like robot. Therefore, this article analyzes the kinematic model of spherical robot and proposes a novel method to generate feasible and achievable paths conforming to kinematic constraints, which can be the initial value of future trajectory tracking control and further optimization. A concept of optimal robot’s minimum area for rotation is also proposed to improve search efficiency and ensure the ability of turning to any orientation by moving forward and backward in a finite number of times within limited areas.

scholarly journals Calibration of Camera and Flash LiDAR System with a Triangular Pyramid Target

2021 ◽  
Vol 11 (2) ◽  
pp. 582
Author(s):  
Zean Bu ◽  
Changku Sun ◽  
Peng Wang ◽  
Hang Dong
Keyword(s):  
Simulated Data ◽  
Real Data ◽  
Calibration Method ◽  
Multiple Sensors ◽  
Triangular Pyramid ◽  
World Coordinate System ◽  
Flash Lidar ◽  
Novel Method ◽  
3D Information ◽  
Incremental Validation

Calibration between multiple sensors is a fundamental procedure for data fusion. To address the problems of large errors and tedious operation, we present a novel method to conduct the calibration between light detection and ranging (LiDAR) and camera. We invent a calibration target, which is an arbitrary triangular pyramid with three chessboard patterns on its three planes. The target contains both 3D information and 2D information, which can be utilized to obtain intrinsic parameters of the camera and extrinsic parameters of the system. In the proposed method, the world coordinate system is established through the triangular pyramid. We extract the equations of triangular pyramid planes to find the relative transformation between two sensors. One capture of camera and LiDAR is sufficient for calibration, and errors are reduced by minimizing the distance between points and planes. Furthermore, the accuracy can be increased by more captures. We carried out experiments on simulated data with varying degrees of noise and numbers of frames. Finally, the calibration results were verified by real data through incremental validation and analyzing the root mean square error (RMSE), demonstrating that our calibration method is robust and provides state-of-the-art performance.

scholarly journals An Automatic Offset Calibration Method for Differential Charge-Based Capacitance Measurement

2021 ◽  
Vol 11 (2) ◽  
pp. 22
Author(s):  
Umberto Ferlito ◽  
Alfio Dario Grasso ◽  
Michele Vaiana ◽  
Giuseppe Bruno
Keyword(s):  
Standard Deviation ◽  
Output Voltage ◽  
Process Variations ◽  
Calibration Method ◽  
Capacitance Measurement ◽  
Effective Technique ◽  
Fully Integrated ◽  
Front End ◽  
Novel Method ◽  
On Chip

Charge-Based Capacitance Measurement (CBCM) technique is a simple but effective technique for measuring capacitance values down to the attofarad level. However, when adopted for fully on-chip implementation, this technique suffers output offset caused by mismatches and process variations. This paper introduces a novel method that compensates the offset of a fully integrated differential CBCM electronic front-end. After a detailed theoretical analysis of the differential CBCM topology, we present and discuss a modified architecture that compensates mismatches and increases robustness against mismatches and process variations. The proposed circuit has been simulated using a standard 130-nm technology and shows a sensitivity of 1.3 mV/aF and a 20× reduction of the standard deviation of the differential output voltage as compared to the traditional solution.

scholarly journals A Comparison of Two Sensory Panels Trained with Different Feedback Calibration Range Specifications via Sensory Description of Five Beers

Foods ◽  
2019 ◽  
Vol 8 (11) ◽  
pp. 534 ◽  
Author(s):  
Line Elgaard ◽  
Line A. Mielby ◽  
Helene Hopfer ◽  
Derek V. Byrne
Keyword(s):  
Average Distance ◽  
Calibration Method ◽  
The Self ◽  
Use Of Self ◽  
Fixed Target ◽  
Immediate Feedback ◽  
Leader Following ◽  
Novel Method ◽  
Standard Deviations ◽  
Sensory Profiles

Feedback on panel performance is traditionally provided by the panel leader, following an evaluation session. However, a novel method for providing immediate feedback to panelists was proposed, the Feedback Calibration Method (FCM). The aim of the current study was to compare the performance of two panels trained by using FCM with two different approaches for ranges calibration, namely self-calibrated and fixed ranges. Both panels were trained using FCM for nine one-hour sessions, followed by a sensory evaluation of five beer samples (in replicates). Results showed no difference in sample positioning in the sensory space by the two panels. Furthermore, the panels’ discriminability was also similar, while the self-calibrated panel had the highest repeatability. The results from the average distance from target and standard deviations showed that the self-calibrated panel had the lowest distance from target and standard deviation throughout all sessions. However, the decrease in average distance from target and standard deviations over training sessions was similar among panels, meaning that the increase in performance was similar. The fact that both panels had a similar increase in performance and yielded similar sensory profiles indicates that the choice of target value calibration method is unimportant. However, the use of self-calibrated ranges could introduce an issue with the progression of the target scores over session, which is why the fixed target ranges should be applied, if available.

scholarly journals Camera and inertial sensor fusion for the PnP problem: algorithms and experimental results

2021 ◽  
Vol 32 (4) ◽  
Author(s):  
Luigi D’Alfonso ◽  
Emanuele Garone ◽  
Pietro Muraca ◽  
Paolo Pugliese
Keyword(s):  
Pose Estimation ◽  
Good Accuracy ◽  
Inertial Sensor ◽  
Experimental Tests ◽  
Inertial Measurement Units ◽  
Magnetic Vector ◽  
Inertial Measurement ◽  
The Earth ◽  
Measurement Units ◽  
Position And Orientation

AbstractIn this work, we face the problem of estimating the relative position and orientation of a camera and an object, when they are both equipped with inertial measurement units (IMUs), and the object exhibits a set of n landmark points with known coordinates (the so-called Pose estimation or PnP Problem). We present two algorithms that, fusing the information provided by the camera and the IMUs, solve the PnP problem with good accuracy. These algorithms only use the measurements given by IMUs’ inclinometers, as the magnetometers usually give inaccurate estimates of the Earth magnetic vector. The effectiveness of the proposed methods is assessed by numerical simulations and experimental tests. The results of the tests are compared with the most recent methods proposed in the literature.

TIME-TO-CONTACT INFORMATION ESTIMATION FOR MONOCULAR MOBILE ROBOTS

2008 ◽  
Vol 05 (03) ◽  
pp. 223-233 ◽  
Author(s):  
RONG LIU ◽  
MAX Q. H. MENG
Keyword(s):  
Mobile Robots ◽  
Optical Flow ◽  
Active Contour Model ◽  
Visual Navigation ◽  
Moving Object ◽  
Time To Contact ◽  
Temporal Derivative ◽  
Novel Method ◽  
Derivatives Of ◽  
Flow Experiments

Time-to-contact (TTC) provides vital information for obstacle avoidance and for the visual navigation of a robot. In this paper, we present a novel method to estimate the TTC information of a moving object for monocular mobile robots. In specific, the contour of the moving object is extracted first using an active contour model; then the height of the motion contour and its temporal derivative are evaluated to generate the desired TTC estimates. Compared with conventional techniques employing the first-order derivatives of optical flow, the proposed estimator is less prone to errors of optical flow. Experiments using real-world images are conducted and the results demonstrate that the developed method can successfully achieve TTC with an average relative error (ARVE) of 0.039 with a single calibrated camera.

A Novel Method for Preoperative 6D Pose Estimation of Rat Skull Based on 3D Vision Techniques

2021 ◽  
Author(s):  
Yujun Wu ◽  
Hanwei Chen ◽  
Bo Han ◽  
Chao Liu ◽  
Xinjun Sheng
Keyword(s):  
Pose Estimation ◽  
3D Vision ◽  
Novel Method

GUESS: projecting machine learning scores to well-calibrated probability estimates for clinical decision-making

2018 ◽  
Vol 35 (14) ◽  
pp. 2458-2465 ◽  
Author(s):  
Johanna Schwarz ◽  
Dominik Heider
Keyword(s):  
Machine Learning ◽  
Decision Support ◽  
Clinical Decision Support ◽  
State Of The Art ◽  
Clinical Decision Support Systems ◽  
Clinical Decision ◽  
Calibration Method ◽  
Probability Estimates ◽  
Calibration Methods ◽  
Novel Method

Abstract Motivation Clinical decision support systems have been applied in numerous fields, ranging from cancer survival toward drug resistance prediction. Nevertheless, clinical decision support systems typically have a caveat: many of them are perceived as black-boxes by non-experts and, unfortunately, the obtained scores cannot usually be interpreted as class probability estimates. In probability-focused medical applications, it is not sufficient to perform well with regards to discrimination and, consequently, various calibration methods have been developed to enable probabilistic interpretation. The aims of this study were (i) to develop a tool for fast and comparative analysis of different calibration methods, (ii) to demonstrate their limitations for the use on clinical data and (iii) to introduce our novel method GUESS. Results We compared the performances of two different state-of-the-art calibration methods, namely histogram binning and Bayesian Binning in Quantiles, as well as our novel method GUESS on both, simulated and real-world datasets. GUESS demonstrated calibration performance comparable to the state-of-the-art methods and always retained accurate class discrimination. GUESS showed superior calibration performance in small datasets and therefore may be an optimal calibration method for typical clinical datasets. Moreover, we provide a framework (CalibratR) for R, which can be used to identify the most suitable calibration method for novel datasets in a timely and efficient manner. Using calibrated probability estimates instead of original classifier scores will contribute to the acceptance and dissemination of machine learning based classification models in cost-sensitive applications, such as clinical research. Availability and implementation GUESS as part of CalibratR can be downloaded at CRAN.

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