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A multi-camera dense RGB-D SLAM (simultaneous localization and mapping) system has the potential both to speed up scene reconstruction and to improve localization accuracy, thanks to multiple mounted sensors and an enlarged effective field of view. To effectively tap the potential of the system, two issues must be understood: first, how to calibrate the system where sensors usually shares small or no common field of view to maximally increase the effective field of view; second, how to fuse the location information from different sensors. In this work, a three-Kinect system is reported. For system calibration, two kinds of calibration methods are proposed, one is suitable for system with inertial measurement unit (IMU) using an improved hand–eye calibration method, the other for pure visual SLAM without any other auxiliary sensors. In the RGB-D SLAM stage, we extend and improve a state-of-art single RGB-D SLAM method to multi-camera system. We track the multiple cameras’ poses independently and select the one with the pose minimal-error as the reference pose at each moment to correct other cameras’ poses. To optimize the initial estimated pose, we improve the deformation graph by adding an attribute of device number to distinguish surfels built by different cameras and do deformations according to the device number. We verify the accuracy of our extrinsic calibration methods in the experiment section and show the satisfactory reconstructed models by our multi-camera dense RGB-D SLAM. The RMSE (root-mean-square error) of the lengths measured in our reconstructed mode is 1.55 cm (similar to the state-of-art single camera RGB-D SLAM systems). [ABSTRACT FROM AUTHOR]

The errors of inertial sensors affect the navigation accuracy of the strapdown inertial navigation system (SINS) and are accumulated over time in nature. In order to continuously maintain the high navigation accuracy of vehicles for a long time period, an initial alignment and self-calibration is necessary after the SINS starts. Additionally, the observability analysis is one of the key techniques during the initial alignment and self-calibration process. For marine systems, the observability of inertial sensor errors is extremely low, as their motion states are always slow. Therefore, studying the rotating SINS is urgent. Since traditional analysis methods have their limitations, the global observation analysis method was used in this paper. On the basis of this method, the relationship between the observability and the kinestate of the rotating SINS has been established. After the discussion about the factors that affect the observability in detail, the design principle of the initial alignment and self-calibration rotating scheme, which is appropriate for marine systems, id proposed. With the proposed principle, a novel initial alignment and self-calibration method, named the eight-position rotating scheme, is designed. Simulations and experiments are carried out to verify its performance. The results have shown that compared with other rotating schemes and the static state, the estimated accuracy of the eight-position scheme rotating about axes x and y was the best, and the position error was significantly reduced with this new rotating scheme. The feasibility and effectiveness of the proposed design principle and the rotating scheme were verified. [ABSTRACT FROM AUTHOR]

A new algorithm called Huber-based iterated divided difference filtering (HIDDF) is derived and applied to cooperative localization of autonomous underwater vehicles (AUVs) supported by a single surface leader. The position states are estimated using acoustic range measurements relative to the leader, in which some disadvantages such as weak observability, large initial error and contaminated measurements with outliers are inherent. By integrating both merits of iterated divided difference filtering (IDDF) and Huber's M-estimation methodology, the new filtering method could not only achieve more accurate estimation and faster convergence contrast to standard divided difference filtering (DDF) in conditions of weak observability and large initial error, but also exhibit robustness with respect to outlier measurements, for which the standard IDDF would exhibit severe degradation in estimation accuracy. The correctness as well as validity of the algorithm is demonstrated through experiment results. [ABSTRACT FROM AUTHOR]

In this paper, an improved inertial frame alignment algorithm for a marine SINS under mooring conditions is proposed, which significantly improves accuracy. Since the horizontal alignment is easy to complete, and a characteristic of gravity is that its component in the horizontal plane is zero, we use a clever method to improve the conventional inertial alignment algorithm. Firstly, a large misalignment angle model and a dimensionality reduction Gauss-Hermite filter are employed to establish the fine horizontal reference frame. Based on this, the projection of the gravity in the body inertial coordinate frame can be calculated easily. Then, the initial alignment algorithm is accomplished through an inertial frame alignment algorithm. The simulation and experiment results show that the improved initial alignment algorithm performs better than the conventional inertial alignment algorithm, and meets the accuracy requirements of a medium-accuracy marine SINS. [ABSTRACT FROM AUTHOR]

Two different coarse alignment algorithms for Fiber Optic Gyro (FOG) Inertial Navigation System (INS) based on inertial reference frame are discussed in this paper. Both of them are based on gravity vector integration, therefore, the performance of these algorithms is determined by integration time. In previous works, integration time is selected by experience. In order to give a criterion for the selection process, and make the selection of the integration time more accurate, optimal parameter design of these algorithms for FOG INS is performed in this paper. The design process is accomplished based on the analysis of the error characteristics of these two coarse alignment algorithms. Moreover, this analysis and optimal parameter design allow us to make an adequate selection of the most accurate algorithm for FOG INS according to the actual operational conditions. The analysis and simulation results show that the parameter provided by this work is the optimal value, and indicate that in different operational conditions, the coarse alignment algorithms adopted for FOG INS are different in order to achieve better performance. Lastly, the experiment results validate the effectiveness of the proposed algorithm. [ABSTRACT FROM AUTHOR]