Accurate navigation is essential for autonomous robots and vehicles. In recent years, the integration of the Global Navigation Satellite System (GNSS), Inertial Navigation System (INS), and camera has garnered considerable attention due to its robustness and high accuracy in diverse environments. However, leveraging the full capacity of GNSS is cumbersome because of the diverse choices of formulations, error models, satellite constellations, signal frequencies, and service types, which lead to different precision, robustness, and usage dependencies. To clarify the capacity of GNSS algorithms and accelerate the development efficiency of employing GNSS in multi-sensor fusion algorithms, we open source the GNSS/INS/Camera Integration Library (GICI-LIB), together with detailed documentation and a comprehensive land vehicle dataset. A factor graph optimization-based multi-sensor fusion framework is established, which combines almost all GNSS measurement error sources by fully considering temporal and spatial correlations between measurements. The graph structure is designed for flexibility, making it easy to form any kind of integration algorithm. For illustration, RTK, PPP, and four Real-Time Kinematic (RTK)-based algorithms from GICI-LIB are evaluated using our dataset and public datasets. Results confirm the potential of the GICI system to provide continuous precise navigation solutions in a wide spectrum of urban environments.

The flexible carrying mode of smartphone brings challenge for Pedestrian Dead Reckoning (PDR) especially for heading estimation with build-in sensors. This paper focuses on POCKET mode and SWING mode and analyzes the correlation between smartphone’s rotational motion and pedestrian’s walking cycle states and walking direction. Based on the analysis, we propose to use the rotation vector sensor data of smartphone within one walking step to estimate the pedestrian’s heading. For POCKET mode, heading is estimated by an improved rotational approach (IRA). A jitter detection algorithm is proposed to extract leg flexion interval. Stable walking direction without 180° ambiguity is obtained from the averaged rotation axis. For SWING mode, a single-point (SP) method is proposed. Heading is estimated from the direction of smartphone’s y-axis when it is closest to the horizontal plane. The algorithms are validated with data collected by HUAWEI mate 10 smartphone. The RMS errors are less than 4.37° and 3.38° for POCKET and SWING mode respectively. Superior to previous heading estimation algorithms, our method can converge within one single walking step for both carrying modes without 180° ambiguity.

Using a standalone camera for pose estimation has been quite a standard task. However, the point correspondence-based algorithms require at least four feature points in the field of view. This paper considers the situation that there are only two feature points. Focusing on the attitude estimation, we propose to fuse a camera with low-cost inertial sensors based on a nonlinear complementary filter design. An implicit geometry measurement model is derived using two feature points in an image. This geometry measurement is fused with the angle rate measurement and vector measurement from inertial sensors using the proposed nonlinear complementary filter with only two parameters to be adjusted. The proposed nonlinear complementary filter is posed directly on the special orthogonal group SO(3). Based on the theory of nonlinear system stability analysis, the proposed filter ensures locally asymptotic stability. A quaternion-based discrete implementation of the filter is also given in this paper for computational efficiency. The proposed algorithm is validated using a smartphone with built-in inertial sensors and a rear camera. The experimental results indicate that the proposed algorithm outperforms all the compared counterparts in estimated accuracy and provides competitive computational complexity.

The advanced receiver autonomous integrity monitoring (advanced RAIM, ARAIM) is the next generation of RAIM which is widely used in civil aviation. However, the current ARAIM needs to evaluate hundreds of subsets, which results in huge computational loads. In this paper, a method using the subset excluding entire constellation to evaluate the single satellite fault subsets and the simultaneous multiple satellites fault subsets is presented. The proposed ARAIM algorithm is based on the tight integration of the global navigation satellite system (GNSS) and inertial navigation system (INS). The number of subsets that the proposed GNSS/INS ARAIM needs to consider is about 2% of that of the current ARAIM, which reduces the computational load dramatically. The detailed fault detection (FD) process and fault exclusion (FE) process of the proposed GNSS/INS ARAIM are provided. Meanwhile, the method to obtain the FD-only integrity bound and the after-exclusion integrity bound is also presented in this paper. The simulation results show that the proposed GNSS/INS ARAIM is able to find the failing satellite accurately and its integrity performance is able to meet the integrity requirements of CAT-I precision approach.

Integrity, a safety-of-life framework from civil aviation for satellite navigation, is greatly under-explored in visual navigation. A new two-factor approach to rejecting measurement outliers is proposed for navigation integrity in stereo visual odometry (VO). In contrast to other treatments using reprojection error as measurement residuals, our choice of landmark matching error inherently connects navigation solutions and integrity monitoring. We propose two methods to detect large measurement residuals that cannot otherwise be identified by existing outlier rejection methods in state-of-the-art VO pipelines. By rejecting these outliers, measurement residuals can be bounded by the distribution overbounding method that provides fundamental inputs for integrity computations. We evaluate our methods using an open-source data set. Overbounding performance is improved in terms of tightness, computational efficiency, and most important of all, scenario tolerance. This could be a good starting point for developing future integrity monitoring algorithms for visual navigation and in particular, stereo VO.