Quality analysis of digital photogrammetric models obtained in low light conditions

Vystrchil, M. G.; SUKHOV, Arseniy Konstantinovich; Novozhenin, S. U.; Popov, A.; Guba, S. A.

doi:10.1088/1742-6596/1661/1/012089

Cited by 2 publications

(1 citation statement)

References 4 publications

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“…The underground mining operation is an intensive process that requires constant updating of the layout plan, as well as roof subsidence monitoring. Automation of this work with the help of robotic equipment and novel technologies, such as computer vision [2], predictive model control [3], machine learning [4] and neural networks [5,6], will increase the efficiency of the mining process, in general, [7][8][9][10], and mine surveying, in particular, [11][12][13].…”

Section: Introductionmentioning

confidence: 99%

Error State Extended Kalman Filter Localization for Underground Mining Environments

2023

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The article addresses the issue of mobile robotic platform positioning in GNSS-denied environments in real-time. The proposed system relies on fusing data from an Inertial Measurement Unit (IMU), magnetometer, and encoders. To get symmetrical error gauss distribution for the measurement model and achieve better performance, the Error-state Extended Kalman Filter (ES EKF) is chosen. There are two stages of vector state determination: vector state propagation based on accelerometer and gyroscope data and correction by measurements from additional sensors. The error state vector is composed of the velocities along the x and y axes generated by combining encoder data and the orientation of the magnetometer around the axis z. The orientation angle is obtained from the magnetometer directly. The key feature of the algorithm is the IMU measurements’ isolation from additional sensor data, with its further summation in the correction step. Validation is performed by a simulation in the ROS (Robot Operating System) and the Gazebo environment on the grounds of the developed mathematical model. Trajectories for the ES EKF, Extended Kalman Filter (EKF), and Unscented Kalman Filter (UKF) algorithms are obtained. Absolute position errors for all trajectories are calculated with an EVO package. It is shown that using the simplified version of IMU’s error equations allows for the achievement of comparable position errors for the proposed algorithm, EKF and UKF.

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Section: Introductionmentioning

confidence: 99%

Error State Extended Kalman Filter Localization for Underground Mining Environments

2023

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Creating highly informative rasters based on laser scanning and aerial photography data

Valkov

Vinogradov

Valkova

et al. 2022

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The paper is focused on scenario and terrain modeling using the results of aerial laser scanning combined with digital aerial photography. The advantages and disadvantages of these technologies are discussed in regard to the construction of large-scale topographic maps. The generalized sequence of cameral processing aerial survey data is investigated. Based on our research, we believe that developing a combined technique of presenting Lidar survey and aerial photography materials is feasible; it could simplify and speed up the operator`s (cartographer`s) work. The novelty of the research is the formation of algorithms for creating original raster images containing more information on the terrain in each section than the orthophotoplane familiar to the interested user. The criteria for the object composition of materials are worked out taking into account the specifics of the methods under consideration, variants of information combinations are formulated for broader opportunities of analyzing and interpreting the data on the territory. Various approaches to the implementation of these ideas are shown. Examples of testing developments are given.

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Quality analysis of digital photogrammetric models obtained in low light conditions

Cited by 2 publications

References 4 publications

Error State Extended Kalman Filter Localization for Underground Mining Environments

Error State Extended Kalman Filter Localization for Underground Mining Environments

Creating highly informative rasters based on laser scanning and aerial photography data

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