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This paper presents an extended version of a previously published Bayesian algorithm for the automatic correction of the positions of the equipment on the map with simultaneous mobile object trajectory localization (SLAM) in underground mine environment represented by undirected graph. The proposed extended SLAM algorithm requires much less preliminary data on possible equipment positions and uses an additional resample move algorithm to significantly improve the overall performance.
During the day-to-day exploitation of localization systems in mines, the technical staff tends to incorrectly rearrange radio equipment: positions of devices may not be accurately marked on a map or their positions may not correspond to the truth. This situation may lead to positioning inaccuracies and errors in the operation of the localization system.This paper presents two Bayesian algorithms for the automatic corrections of positions of the equipment on the map using trajectories restored by the inertial measurement units mounted to mobile objects, like pedestrians and vehicles. As a basis, a predefined map of the mine represented as undirected weighted graph was used as input. The algorithms were implemented using the Simultaneous Localization and Mapping (SLAM) approach.The results prove that both methods are capable to detect misplacement of access points and to provide corresponding corrections. The discrete Bayesian filter outperforms the unscented Kalman filter, which, however, requires more computational power.
When designing and installing Indoor Positioning Systems, several interrelated tasks have to be solved to find an optimum placement of the Access Points. For this purpose, a mathematical model for a predefined number of access points indoors is presented. Two iterative algorithms for the minimization of localization error of a mobile object are described. Both algorithms use local search technique and signal level probabilities. Previously registered signal strengths maps were used in computer simulation.
Ultra wide band (UWB) signals are well suited both for short-range wireless communication and for high-precision localization applications. Channel impulse response (CIR) analysis in UWB systems is a major element in localization estimation. In this paper, practical aspects of CIR are presented. I.e. a technique for the construction of the accumulated echo-gram of a multipath delayed signal is proposed. Decawave hardware was used to demonstrate the technique of analysis of fine structure of signals with a sub-nanosecond resolution. Temporal stability, reliability and two-way characteristics of such echo-grams are discussed as well. The results of using two EVK1000 radio modules as a radar installation to detect a target in indoor environments prove that a low cost UWB intrusion detection and through-the-wall-vision systems might be developed using the proposed technique.
The paper describes the hardware and software architecture of the developed multi MEMS sensor prototype module, consisting of ARM Cortex M4 STM32F446 microcontroller unit, five 9-axis inertial measurement units MPU9255 (3D accelerometer, 3D gyroscope, 3D magnetometer and temperature sensor) and a BMP280 barometer. The module is also equipped with WiFi wireless interface (Espressif ESP8266 chip). The module is constructed in the form of a truncated pyramid. Inertial sensors are mounted on a special basement at different angles to each other to eliminate hardware sensors drifts and to provide the capability for self-calibration. The module fuses information obtained from all types of inertial sensors (acceleration, rotation rate, magnetic field and air pressure) in order to calculate orientation and trajectory. It might be used as an Inertial Measurement Unit, Vertical Reference Unit or Attitude and Heading Reference System.