This paper presents range-based 6-DoF Monte Carlo SLAM with a gradient-guided particle update strategy. While non-parametric state estimation methods, such as particle filters, are robust in situations with high ambiguity, they are known to be unsuitable for high-dimensional problems due to the curse of dimensionality. To address this issue, we propose a particle update strategy that improves the sampling efficiency by using the gradient information of the likelihood function to guide particles toward its mode. Additionally, we introduce a keyframe-based map representation that represents the global map as a set of past frames (i.e., keyframes) to mitigate memory consumption. The keyframe poses for each particle are corrected using a simple loop closure method to maintain trajectory consistency. The combination of gradient information and keyframe-based map representation significantly enhances sampling efficiency and reduces memory usage compared to traditional RBPF approaches. To process a large number of particles (e.g., 100,000 particles) in real-time, the proposed framework is designed to fully exploit GPU parallel processing. Experimental results demonstrate that the proposed method exhibits extreme robustness to state ambiguity and can even deal with kidnapping situations, such as when the sensor moves to different floors via an elevator, with minimal heuristics.

In this paper, we propose a non-parametric method for estimating the posterior distribution of global positioning satellite systems (GNSS) integer ambiguity. It is difficult to estimate the posterior probability of discrete integer ambiguities directly from carrier phase observations due to the unclear domain definition. We thus introduce a positional likelihood field that accumulates the ambiguity function method values in the position space and then estimate the integer ambiguity distributions by marginalizing the likelihood over the entire position. Defining the positional likelihood field in the position space facilitates carrier phase likelihood accumulation. To correctly estimate the posterior distribution, however, a sufficient density of samples is required, which results in a large computational cost. The proposed method enables large-scale sampling by taking advantage of GPU parallel processing. Experimental results demonstrate that the proposed method enables accurate and robust estimation of integer ambiguity distributions, contributing to improved centimeter-level position estimation accuracy. In addition, the histograms provide quantitative evidence of events in urban environments where integer ambiguity is not uniquely determined.

Autonomous driving of vehicles and robots requires highly accurate position information, and RTK-GNSS is expected to be utilized for this purpose. In this paper, we propose a robust and real-time operation method by introducing graph optimization into the integrated RTK-GNSS/IMU method. The proposed method is an extension of a method using vehicle trajectories that can estimate positions with lane-level accuracy even in urban areas. The position is estimated by removing GNSS multipaths from the shape of a vehicle trajectory of several hundred meters and averaging the remaining GNSS results. This method does not take into account the errors in the vehicle trajectory and cannot fully benefit from the high accuracy positioning solution of RTK-GNSS. To solve this problem, we introduce graph optimization to the base method, which treats the error state as a probabilistic model. However, general graph optimization methods have problems with processing time and outlier elimination. The proposed method solves these problems by restricting the time series data to be optimized and using a two-step optimization structure. Evaluations show that the proposed method is effective because it satisfies the requirements for real-time operation and improves accuracy compared to conventional methods.

This study proposes a method for estimating the positions of vehicles in urban environments with high accuracy. We employ satellite positioning by GNSS for position estimation. Real-time kinematic-global navigation satellite systems (RTK-GNSS) with high precision in satellite positioning can estimate positions with centimeter-scale accuracy. However, in urban areas, the position estimation performance deteriorates owing to multipath errors. Therefore, we propose a method to improve the positioning results by increasing the robustness against multipath using vehicle trajectory. The vehicle trajectory estimates the travel route using the attitude angle and speed. Attitude angles are heading, pitching and slip angle. Trajectories can be generated with 0.5m error performance per 100m. In the proposed method, the trajectory is used as a constraint to solve the multipath of RTK-GNSS. In the evaluation test, the ratio of high-accuracy position estimation improved by up to approximately 30% compared to the conventional method. It is assumed that this method can enhance the development of self-driving cars, AGV control and SLAM technology by eliminating errors and calculating reliability.