An Onsite Calibration Method for MEMS-IMU in Building Mapping Fields

Abstract

Light detection and ranging (LiDAR) is one of the popular technologies to acquire critical information for building information modelling. To allow an automatic acquirement of building information, the first and most important step of LiDAR technology is to accurately determine the important gesture information that micro electromechanical (MEMS) based inertial measurement unit (IMU) sensors can provide from the moving robot. However, during the practical building mapping, serious errors may happen due to the inappropriate installation of a MEMS-IMU. Through this study, we analyzed the different systematic errors, such as biases, scale errors, and axial installation deviation, that happened during the building mapping, based on a robot equipped with MEMS-IMU. Based on this, an error calibration model was developed. The problems of the deviation between the calibrated and horizontal planes were solved by a new sampling method. For this method, the calibrated plane was rotated twice; the gravity acceleration of the six sides of the MEMS-IMU was also calibrated by the practical values, and the whole calibration process was completed after solving developed model based on the least-squares method. Finally, the building mapping was then calibrated based on the error calibration model, and also the Gmapping algorithm. It was indicated from the experiments that the proposed model is useful for the error calibration, which can increase the prediction accuracy of yaw by 1–2° based on MEMS-IMU; the mapping results are more accurate when compared to the previous methods. The research outcomes can provide a practical basis for the construction of the building information modelling model.

An incremental LiDAR/POS online calibration method

“…The different localization solving principles based on IMU and LiDAR allow the error models of trajectories to be independent and the residual optimization between trajectories to be able to obtain convergent calibration parameters. Li et al [30] used GP (Gaussian process) regression to model the independent timestamp delay of IMU, but this method relies on the correlation of scanned data and can only be effective in the pre-built map. Lv et al [31] propose a continuous time trajectory formula based on B-spline to solve the problem of asynchronous measurement in highspeed scenes.…”

UAV Autonomous Localization Using Macro-Features Matching with a CAD Model

“…Prior to the UAV flight, a Building Information Model (BIM) [14] is loaded using Open Asset Import Library (Assimp) [15] which provides a unified method of accessing the data within. A BIM is a 3D CAD model typically produced during the planning phase of a construction project and includes information about macro-features as well as their locations as shown in Figure 1.…”

“…where xi and yi are the corresponding vectors originating at the respective centroids. The 3 × 3 covariance matrix is then computed using: (14) where X and Y are the vectors of dimension 3 and W = diag ( , , . .…”

Modeling and Compensation of Inertial Sensor Errors in Measurement Systems

“…In [14], error analysis and modeling of consumer-grade inertial sensors was carried out and the bias-corrected output of tactical-grade inertial navigation equipment was used as a reference true value to perform online calibration of consumergrade inertial navigation equipment. In [15], a study was performed of the online calibration method of the microelectromechanical system (MEMS) IMU installed on a robot for the surveying and mapping of buildings. The calibration method was found to improve the output accuracy of the attitude information of the MEMS IMU.…”