This paper presents an efficient method for updating particles in a particle filter (PF) to address the position estimation problem when dealing with sharp-peaked likelihood functions derived from multiple observations. Sharp-peaked likelihood functions commonly arise from millimeter-accurate distance observations of carrier phases in the global navigation satellite system (GNSS). However, when such likelihood functions are used for particle weight updates, the absence of particles within the peaks leads to all particle weights becoming zero. To overcome this problem, in this study, a straightforward and effective approach is introduced for updating particles when dealing with sharp-peaked likelihood functions obtained from multiple observations. The proposed method, termed as the multiple update PF, leverages prior knowledge regarding the spread of distribution for each likelihood function and conducts weight updates and resampling iteratively in the particle update process, prioritizing the likelihood function spreads. Experimental results demonstrate the efficacy of our proposed method, particularly when applied to position estimation utilizing GNSS pseudorange and carrier phase observations. The multiple update PF exhibits faster convergence with fewer particles when compared to the conventional PF. Moreover, vehicle position estimation experiments conducted in urban environments reveal that the proposed method outperforms conventional GNSS positioning techniques, yielding more accurate position estimates.

A large-scale dump truck that automatically transports earth and sand in cooperation with a human-operated backhoe is of interest to the construction industry. A human-operated dump truck generally drives slightly past the desired loading position and then backs up to it for loading the sediment. The turning and loading positions are subjectively decided according to the working posture of the backhoe and the surrounding environment, and the safety margin of cooperative works. Backhoe operators want to perform the same maneuvers for human-operated/automated dump trucks. The movements of the autonomous vehicle should be similar to those of a human-operated one. However, it is difficult to derive a human-like path that does more than minimize costs. This study proposes a path-planning method that generates a path including a turning back, according to the changing backhoe position and surrounding conditions. We modeled the positional relationship during loading between a backhoe and dump truck, determining the loading and turning positions and related parameters from operational data collected in trials with human-operated construction vehicles. The proposed method allowed the autonomous dump truck path to resemble a human-like one. The authors have retrofitted an existing large-scale six-wheeled dump truck for automatic operation. Automatic loading in cooperation with a human-operated backhoe was realized all 17 times using the retrofitted dump. The average stopping accuracy was 0.57 m and 9.7°.

Backhoe loads sediment onto the bed of dump trucks during earthmoving work. The prediction of backhoe loading time is essential for ensuring safe cooperation between the backhoe and dump trucks. However, it is difficult to predict the instant at which the backhoe is ready to load sediment, because of the similarity in motions observed during gathering sediment. Moreover, since operators have different skill levels, the prediction requires a unique model for each operator. In this study, we attempt to predict the instant at which the backhoe is ready to load sediment into the dump truck. For this purpose, the beta-process hidden Markov model (BP-HMM) is employed to build a backhoe motion model for a specific operator. Time series data of backhoe loading motions for crushed rocks and wood chips, which were measured using 6-axis inertial measurement unit (IMU) sensors equipped at the cab, boom, and arm of the backhoe, were used for modeling with the BP-HMM. Several primitive motions of the backhoe, which occur at the completion of preparation before the loading process begins, were discovered as a result of the motion modeling based on the BP-HMM. We developed the prediction of the instant using three primitive motions. At best, the proposed method could predict the instant with a probability of 67% and 100%, at 6.0 s and 0.7 s before the loading motions began, respectively. This phased prediction can be used to reduce the idle time and risk for dump trucks during earthmoving work with the backhoe.

Small-sized unmanned aerial vehicles (UAVs) have been widely investigated for use in a variety of applications such as remote sensing and aerial surveying. Direct three-dimensional (3D) mapping using a small-sized UAV equipped with a laser scanner is required for numerous remote sensing applications. In direct 3D mapping, the precise information about the position and attitude of the UAV is necessary for constructing 3D maps. In this study, we propose a novel and robust technique for estimating the position and attitude of small-sized UAVs by employing multiple low-cost and light-weight global navigation satellite system (GNSS) antennas/receivers. Using the “redundancy'' of multiple GNSS receivers, we enhance the performance of real-time kinematic (RTK)-GNSS by employing single-frequency GNSS receivers. This method consists of two approaches: hybrid GNSS fix solutions and consistency examination of the GNSS signal strength. The fix rate of RTK-GNSS using single-frequency GNSS receivers can be highly enhanced to combine multiple RTK-GNSS to fix solutions in the multiple antennas. In addition, positioning accuracy and fix rate can be further enhanced to detect multipath signals by using multiple GNSS antennas. In this study, we developed a prototype UAV that is equipped with six GNSS antennas /receivers. From the static test results, we conclude that the proposed technique can enhance the accuracy of the position and attitude estimation in multipath environments. From the flight test, the proposed system could generate a 3D map with an accuracy of 5 cm.

This paper describes outdoor localization for a mobile robot using precise point positioning (PPP) based on the Quasi-Zenith Satellite System (QZSS) L-band Experiment (LEX) signal. For autonomous navigation applications, a real-time kinematic (RTK) global positioning system (GPS) technique is widely used to estimate user position with high-precision accuracy in real time. However, RTK-GPS requires a reference station, and there are data acquisition costs involved in estimating the position. Our approach corrects position error by applying PPP using the QZSS LEX message. PPP can estimate a single receiver position without any reference station or baseline, through use of satellite position fixing and clocks. We developed a method for extracting the QZSS LEX message in real time using a software GNSS receiver. We then constructed the PPP framework based on an LEX message containing the satellite ephemeris and clock errors. Finally, we conducted field experiments to evaluate the accuracy and precision of our proposed method. The experimental results confirmed that our method made a localization precision of 1.29 m possible without using a GNSS reference station.

This paper describes a precision positioning technique that can be applied to vehicles or mobile robots in urban or leafy environments. Currently, the availability of satellite positioning is anticipated to improve because of the presence of various positioning satellites such as GPS of the U.S., GLONASS of Russia and GALILEO of Europe. However, because of the serious impact of multipath on their positioning accuracy in urban or leafy areas, such improvements in the availability of satellite positioning do not necessarily also facilitate high precision positioning. Our proposed technique mitigates GPS and GLONASS multipath by means of an omnidirectional infrared (IR) camera that can eliminate the need for invisible satellites by using IR images. With an IR camera, the sky appears distinctively dark. This facilitates the detection of the borderline between the sky and the surrounding buildings, which are captured in white, because of the difference in the atmospheric transmittance rates between visible light and IR rays. The proposed technique can automatically and robustly mitigate GPS and GLONASS multipath by excluding the invisible satellites. Positioning evaluation was carried out only with visible satellites that have less multipath errors and without using invisible satellites. The evaluation results confirm the effectiveness of the proposed technique and the feasibility of its highly accurate positioning.

This paper describes outdoor localization for a mobile robot using a laser scanner and a three-dimensional (3D) voxel map that is based on outdoor point clouds. A mobile mapping system (MMS) measures outdoor 3D point clouds easily and precisely. The complete 6D state of a mobile robot is estimated by combining dead reckoning and the 3D voxel map. The 2D position and orientation are extended to 3D by using the 3D voxel map and by assuming that the mobile robot remains in continuous contact with the road surface. Our approach applies a particle filter to correct position errors in the laser measurement model for a 3D point cloud space. Field experiments were performed to evaluate the accuracy of our proposed method. Our results confirmed that it is possible to achieve a localization precision of 0.2 m (RMS) using our proposed method.