Simultaneous localization and mapping (SLAM) plays an important role in autonomous driving, indoor robotics and AR/VR. Outdoor SLAM has been widely used with the assistance of LiDAR and Global Navigation Satellite System (GNSS). However, for indoor applications, the commonly used LiDAR sensor does not satisfy the accuracy requirement and the GNSS signals are blocked. Thus, an accurate and reliable 3D sensor and suited SLAM algorithms are required for indoor SLAM. One of the most promising 3D perceiving techniques, fringe projection profilometry (FPP), shows great potential but does not prevail in indoor SLAM. In this paper, we first introduce FPP to indoor SLAM, and accordingly propose suited SLAM algorithms, thus enabling a new FPP-SLAM. The proposed FPP-SLAM can achieve millimeter-level and real-time mapping and localization without any expensive equipment assistance. The performance is evaluated in both simulated controlled and real room-sized scenes. The experimental results demonstrate that our method outperforms other state-of-the-art methods in terms of efficiency and accuracy. We believe this method paves the way for FPP in indoor SLAM applications.

In order to obtain the aerodynamic parameters of a high-spinning projectile, a new parameter identification method is proposed based on the Cubature Kalman Filter (CKF). First of all, the motion equation of the spinning stabilized projectile is established by the 4D trajectory model. Second, unknown parameters are added to the state vector to obtain the augmented state vector. Next, a new filter is designed for the identification of the unknown parameters based on the basic theory of the CKF. Finally, the simulation results of the CKF are compared with those of the Extended Kalman Filter (EKF) and the Unscented Kalman Filter (UKF). The results of comparison show that the CKF method can effectively identify the aerodynamic parameters and the identification error is less than 1.2%. The CKF method has greater accuracy than the EKF and the UKF.

Panoramic 3D measurement becomes increasingly important for fringe projection profilometry (FPP). Traditional physical markers-assisted (PMA) method suffers from inefficiencies and non-complete measurement. An optical markers-assisted (OMA) panoramic 3D method has been recently proposed, which enables accurate, efficient and non-destructive panoramic 3D measurement. In this paper, we give a comprehensive comparison between OMA and PMA, which provides reasonable suggestions for different panoramic 3D measurement applications.

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The traditional hyperbolic range equation model will produce large errors in the spaceborne sliding spotlight synthetic aperture radar with large azimuth time. Although the equivalent acceleration range model (EARM) can deal with the problem of large azimuth scenes well, the interpolation steps from azimuth resampling make the computational burden of algorithm very huge. In response to this problem, this paper improves EARM by dividing the range model into two parts: residual terms of azimuth time and equivalent range expression. The frequency domain imaging algorithm designed for the new range model omits the interpolation step of azimuth resampling. The simulation experiments verify the efficiency and validity of the proposed methodology.