Search

Accurate positioning and navigation play a vital role in vehicle-related applications, such as autonomous driving and precision agriculture. With the rapid development of Global Navigation Satellite Systems (GNSS), Precise Point Positioning (PPP) technique, as a global positioning solution, has been widely applied due to its convenient operation. Nevertheless, the performance of PPP is severely affected by signal interference, especially in GNSS-challenged environments. Inertial Navigation System (INS) aided GNSS can significantly improve the continuity and accuracy of navigation in harsh environments, but suffers from degradation during GNSS outages. LiDAR (Laser Imaging, Detection, and Ranging)-Inertial Odometry (LIO), which has performed well in local navigation, can restrain the divergence of Inertial Measurement Units (IMU). However, in long-range navigation, error accumulation is inevitable if no external aids are applied. To improve vehicle navigation performance, we proposed a tightly coupled GNSS PPP/INS/LiDAR (GIL) integration method, which tightly integrates the raw measurements from multi-GNSS PPP, Micro-Electro-Mechanical System (MEMS)-IMU, and LiDAR to achieve high-accuracy and reliable navigation in urban environments. Several experiments were conducted to evaluate this method. The results indicate that in comparison with the multi-GNSS PPP/INS tightly coupled solution the positioning Root-Mean-Square Errors (RMSEs) of the proposed GIL method have the improvements of 63.0%, 51.3%, and 62.2% in east, north, and vertical components, respectively. The GIL method can achieve decimeter-level positioning accuracy in GNSS partly-blocked environment (i.e., the environment with GNSS signals partly-blocked) and meter-level positioning accuracy in GNSS difficult environment (i.e., the environment with GNSS hardly used). Besides, the accuracy of velocity and attitude estimation can also be enhanced with the GIL method.

If both the coefficient matrix and the observation vector are affected by noise, a total least-squares algorithm should be applied to obtain the solution. However, if they are also contaminated by ...

3D similarity transformation is frequently encountered operation in the field of geodetic data processing, and there are many applications that involve large rotation angles. In previous studies, the errors of the coefficient matrix were usually neglected and a least squares algorithm was applied to calculate the transformation parameters. However, the coefficient matrix is composed of the point coordinates in source coordinate system, i.e., the coefficient matrix is also contaminated by errors. Therefore, a total least squares algorithm should be applied. In this paper, a new method is proposed to address the 3D similarity transformation problem with large rotation angles. Firstly, the scale factor and rotation matrix are put together as the parameter matrix to avoid the rank-defect problem. Then, the translation vector is removed and the multivariate model is constructed. Finally, the constraints are introduced according to the properties of the parameter matrix and the constrained multivariate total least squares algorithm is derived to obtain the transformation parameters. The experimental results show that the proposed method has a high computational efficiency. Resumo: A transformação da similaridade 3D é frequentemente encontrada no campo do processamento geodésico de dados, e há muitas aplicações que envolvem grandes ângulos de rotação. Em estudos anteriores, os erros da matriz de coeficientes foram geralmente negligenciados e um algoritmo de menos quadrados foi aplicado para calcular os parâmetros de transformação. No entanto, a matriz de coeficiente é composta pelas coordenadas de ponto no sistema de coordenadas de origem, ou seja, a matriz de coeficiente também está contaminada por erros. Portanto, um algoritmo total de quadrados mínimos deve ser aplicado. Neste artigo, propõe-se um novo método para abordar o problema de transformação da similaridade 3D com grandes ângulos de rotação. Em primeiro lugar, o fator de escala e a matriz de rotação são montados como a matriz de parâmetros para evitar o problema de defeito de classificação. Em seguida, o vetor de tradução é removido e o modelo multivariado é construído. Finalmente, as restrições são introduzidas de acordo com as propriedades da matriz de parâmetros e o algoritmo multivariado multivariado restrito de menos quadrados é derivado para obter os parâmetros de transformação. Os resultados experimentais mostram que o método proposto tem alta eficiência computacional.