TY - JOUR

T1 - Positioning Based on Integration of Multi-Sensor Systems Using Kalman Filter and Least Square Adjustment

AU - Omidalizarandi, Mohammad

AU - CAO, Zhou

PY - 2013/9

Y1 - 2013/9

N2 - Sensor fusion is to combine different sensor data from different sources in order to make a more accurate model. In this research, different sensors (Optical Speed Sensor, Bosch Sensor, Odometer, XSENS, Silicon and GPS receiver) have been utilized to obtain different kinds of datasets to implement the multi-sensor system and comparing the accuracy of the each sensor with other sensors.The scope of this research is to estimate the current position and orientation of the Van. The Van's position can also be estimated by integrating its velocity and direction over time. To make these components work, it needs an interface that can bridge each other in a data acquisition module. The interface of this research has been developed based on using Labview software environment. Data have been transferred to PC via A/D convertor (LabJack) and make a connection to PC. In order to synchronize all the sensors, calibration parameters of each sensor is determined in preparatory step. Each sensor delivers result in a sensor specific coordinate system that contains different location on the object, different definition of coordinate axes and different dimensions and units. Different test scenarios (Straight line approach and Circle approach) with different algorithms (Kalman Filter, Least square Adjustment) have been examined and the results of the different approaches are compared together.

AB - Sensor fusion is to combine different sensor data from different sources in order to make a more accurate model. In this research, different sensors (Optical Speed Sensor, Bosch Sensor, Odometer, XSENS, Silicon and GPS receiver) have been utilized to obtain different kinds of datasets to implement the multi-sensor system and comparing the accuracy of the each sensor with other sensors.The scope of this research is to estimate the current position and orientation of the Van. The Van's position can also be estimated by integrating its velocity and direction over time. To make these components work, it needs an interface that can bridge each other in a data acquisition module. The interface of this research has been developed based on using Labview software environment. Data have been transferred to PC via A/D convertor (LabJack) and make a connection to PC. In order to synchronize all the sensors, calibration parameters of each sensor is determined in preparatory step. Each sensor delivers result in a sensor specific coordinate system that contains different location on the object, different definition of coordinate axes and different dimensions and units. Different test scenarios (Straight line approach and Circle approach) with different algorithms (Kalman Filter, Least square Adjustment) have been examined and the results of the different approaches are compared together.

KW - Sensor fusion, Kalman Filter, Least Square Adjustment, Calibration, Dead Reckoning

U2 - https://doi.org/10.5194/isprsarchives-XL-1-W3-309-2013

DO - https://doi.org/10.5194/isprsarchives-XL-1-W3-309-2013

M3 - Conference article

VL - XL-1/W3

SP - 309

EP - 314

JO - The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences

JF - The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences

SN - 2194-9034

ER -