Navigation of robotics platform using advanced image processing navigation methods

Beran, Leoš; Chmelar, Petr; Rejfek, Lubos

doi:10.1201/b19241-57

Cited by 3 publications

(3 citation statements)

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“…The DCM correction will be implemented to final version of a robotics platformnavigation of robotics platform using a fusion of visual odometry, Inertial Measurement Unit and mechanical odometers at every wheel. A very big advantage of our visual odometry is precision of position estimation [12]. The final device will be used as a fusion of navigation of the robotics platformwith an optical measurement device to create a 3D space map of dangerous spaces like a ruins, abandoned mines etc.…”

Section: Resultsmentioning

confidence: 99%

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An Increase in Estimation Accuracy Position Determination of Inertial Measurement Units

2016

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Abstract. This paper deals with an increase in measurement accuracy of the Inertial Measurement Units (IMU). In the Inertial Navigation Systems (INS) a fusion of gyroscopes, accelerometers and in some cases magnetometers are typically used. The typical problem of cheap IMU is non-stationary offset and high level of noise. The next problem of IMU is a problem with a bumpy floor. For this case it is necessary to a have high quality chassis to eliminate additional noise. Also, it is possible to eliminate this noise by using some algorithm, but results are still poor. These properties lead to the inaccurate position estimation in the integration process. Even a small offset error leads to a big mistake in position determination and grows quickly with a time. This research is focused on the elimination of these poor properties and increase of accuracy of position estimation using Kalman Filtration.

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Section: Resultsmentioning

confidence: 99%

“…Equations (21) to (25) provide a recursive solution of a model which is described by the equations (11) and (12).…”

Section: X(t) Is State Vector In Time T Y(t) Is a Vector With Measurmentioning

confidence: 99%

An Increase in Estimation Accuracy Position Determination of Inertial Measurement Units

2016

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“…The feature-based matching extracts distinguishing features from the range of images and uses corresponding features to calculate scan alignment. For most robots, the detection of closed loops is realized from camera data [14]. The most common functions of the detector are: SURF; SIFT; GLOH; Shape Context; PCA; Moments; Cross-correlation; and Steerable filters [9].…”

Section: Feature Based Registrationmentioning

confidence: 99%

Survey of Point Cloud Registration Methods and New Statistical Approach

Marek,

Chmelař

2023

Mathematics

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The use of a 3D range scanning device for autonomous object description or unknown environment mapping leads to the necessity of improving computer methods based on identical point pairs from different point clouds (so-called registration problem). The registration problem and three-dimensional transformation of coordinates still require further research. The paper attempts to guide the reader through the vast field of existing registration methods so that he can choose the appropriate approach for his particular problem. Furthermore, the article contains a regression method that enables the estimation of the covariance matrix of the transformation parameters and the calculation of the uncertainty of the estimated points. This makes it possible to extend existing registration methods with uncertainty estimates and to improve knowledge about the performed registration. The paper’s primary purpose is to present a survey of known methods and basic estimation theory concepts for the point cloud registration problem. The focus will be on the guiding principles of the estimation theory: ICP algorithm; Normal Distribution Transform; Feature-based registration; Iterative dual correspondences; Probabilistic iterative correspondence method; Point-based registration; Quadratic patches; Likelihood-field matching; Conditional random fields; Branch-and-bound registration; PointReg. The secondary purpose of this article is to show an innovative statistical model for this transformation problem. The new theory needs known covariance matrices of identical point coordinates. An unknown rotation matrix and shift vector have been estimated using a nonlinear regression model with nonlinear constraints. The paper ends with a relevant numerical example.

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