For the last two decades, low-cost Global Navigation Satellite System (GNSS) receivers have been used in various applications. These receivers are mini-size, less expensive than geodetic-grade ...receivers, and in high demand. Irrespective of these outstanding features, low-cost GNSS receivers are potentially poorer hardwares with internal signal processing, resulting in lower quality. They typically come with low-cost GNSS antenna that has lower performance than their counterparts, particularly for multipath mitigation. Therefore, this research evaluated the low-cost GNSS device performance using a high-rate kinematic survey. For this purpose, these receivers were assembled with an Inertial Measurement Unit (IMU) sensor, which actively transmited data on acceleration and orientation rate during the observation. The position and navigation parameter data were obtained from the IMU readings, even without GNSS signals via the U-blox F9R GNSS/IMU device mounted on a vehicle. This research was conducted in an area with demanding conditions, such as an open sky area, an urban environment, and a shopping mall basement, to examine the device's performance. The data were processed by two approaches: the Single Point Positioning-IMU (SPP/IMU) and the Differential GNSS-IMU (DGNSS/IMU). The Unscented Kalman Filter (UKF) was selected as a filtering algorithm due to its excellent performance in handling nonlinear system models. The result showed that integrating GNSS/IMU in SPP processing mode could increase the accuracy in eastward and northward components up to 68.28% and 66.64%. Integration of DGNSS/IMU increased the accuracy in eastward and northward components to 93.02% and 93.03% compared to the positioning of standalone GNSS. In addition, the positioning accuracy can be improved by reducing the IMU noise using low-pass and high-pass filters. This application could still not gain the expected position accuracy under signal outage conditions.
It has become a consensus to improve the measurement accuracy of microelectromechanical systems (MEMS) inertial measurement units (IMUs) through an IMU array composed of multiple low-cost IMUs. To ...meet the accuracy requirements of the inertial navigation MEMS IMU array, it can be divided into two steps: the first step is to calibrate a single IMU in the array, and the second step is to isomorphically fuse the gyroscopes and accelerometers of multiple IMUs. This article discusses an innovative calibration and fusion technology based on the accompanying test method and weighted fusion technology, which optimizes the two steps to improve the accuracy of the MEMS IMU array. The calibration is more efficient and does not require complex precision calibration equipment. We verified this method as a useful scheme by designing a principle prototype and experimental platform.
The Boreas dataset was collected by driving a repeated route over the course of 1 year, resulting in stark seasonal variations and adverse weather conditions such as rain and falling snow. In total, ...the Boreas dataset includes over 350 km of driving data featuring a 128-channel Velodyne Alpha-Prime lidar, a 360° Navtech CIR304-H scanning radar, a 5MP FLIR Blackfly S camera, and centimetre-accurate post-processed ground truth poses. Our dataset will support live leaderboards for odometry, metric localization, and 3D object detection. The dataset and development kit are available at boreas.utias.utoronto.ca.
Low-cost inertial measurement units (IMUs) are ubiquitously used in the attitude estimation of cell phones and robots. Accurate and robust IMU calibration is required to ensure attitude estimation ...accuracy. This article proposes an accurate and robust equipment-free IMU calibration method. We do not assume gyroscope biases are invariant during calibration compared with conventional methods because low-cost IMUs like MPU6000 have large gyroscope biases instability. We propose introducing biases removal and outlier-aware optimization to alleviate the impact of variant gyroscope biases. Furthermore, we introduce a multiresolution analysis (MRA)-based static detector to detect subtle IMU motion in real data collection. Our detector can detect 84% subtle motions (1° rotations) present in the simulated calibration data while the conventional variance thresholding detector can only detect 31% of them. In addition, we derive a proper data collection method to guide the user to collect data effectively. We benchmark our method with another existing equipment-free method with synthetic and real datasets. The results in synthetic datasets show that our method is 25% more accurate and robust than the existing method. The results in real datasets vouch that our method achieves an estimation of IMU intrinsic parameters comparable to the ground truth. Furthermore, the roll and pitch estimation of MPU6000 using our calibration method are close to (<inline-formula> <tex-math notation="LaTeX">< 0.15 </tex-math></inline-formula>° in ±30°) that of an expensive factory-calibrated IMU in real testing.
Elliptic positioning system offers a precise alternative to global navigation satellite system (GNSS). However, ranging measurements upon a single UAV only delineate the location estimates to a ...spherical region. Data infusion from inertial measurement units (IMUs) may refine these estimates, while its fidelity is undermined by IMUs' lack of self alignment to a specified reference frame. In this paper, we explore the minimal number of assisted UAVs or anchors for absolute positioning, i.e., location and alignment in a fixed frame, during complete GNSS outages. We first prove that the observability establishes under a UAV in a 3-D trajectory or a pair of static anchors. The two numbers are new theoretical limit, significantly lower than the three anchors in 2-D or four in 3-D scenarios required by traditional theorems. We propose a sequential scheme for the multi-parameter estimation problem ensuring rapid convergence. An iterative solution is derived, flexible to UAV and anchor-based configurations, that provides instant location updates free of computational overhead. Thereafter, we also circumvent NLOS effects by employing inverse estimation of range. Accordingly, we devise a tiered positioning framework that commences with a location-unknown UAV to first cooperate with LOS anchors, and then extend the service to UE via a single NLOS link outside anchors' coverage. In the experiments, the proposed scheme reaches (10 −2 )° orientation alignment and centimeter-level accuracy in NLOS scenarios, which attains the Cramer-Rao lower bound (CRLB) accuracy. Moreover, the accuracy notably exceeds the noise level of ranging measurements at high sampling rate, and also shows robustness against local clock drifting.
In the integrated navigation of global navigation satellite system (GNSS) and Inertial navigation system (INS), the Kalman filter is frequently employed for state estimation. GNSS signals are subject ...to interference from the outside environment, and it is difficult for the classical Kalman filter to handle observations with outliers. Numerous robust filters have been proposed based on the maximum correntropy criterion (MCC), which is insensitive to non-Gaussian noise, to address the issue of state estimation in the presence of outliers. However, a fixed prior covariance will restrict the use of a maximum correntropy criterion Kalman filter (MCKF) in integrated navigation systems because GNSS signals can be affected by both time-varying noise and outliers. Existing filters consider the prior measurement noise covariance matrix (MNCM) as a deterministic matrix and can only amplify signals unidirectionally during iteration cycles. The weight of effective observations is reduced despite these filters having strong robustness. To overcome this shortcoming, this paper proposes a τ -based maximum correntropy criterion Kalman filter (τ -MCKF). A strategy based on the Chisquare test is designed to optimize the covariance of GNSS observations. By introducing τ to bi-directionally adjust the initial MNCM, τ -MCKF makes full use of effective observations. Then, the τ -corrected MNCM is used to weigh different elements of the innovation vector, and a robust estimation is calculated based on the multiple-outlier robust Kalman filter (MORKF) framework. The GNSS/INS integrated navigation system is taken as an example to verify the effectiveness of the filter through simulation and vehicular experiments.
Display omitted
•A VIO, CYVIO, is proposed to acquire real-time motion information.•A velocity output algorithm is constructed.•The effectiveness of the VIO motion information acquisition method is ...verified.
Profiling tea harvester moving along the planting ridge can cut the tea leaves by adjusting the height of the cutters according to the tea bush profile. The harvester is usually driven manually or remotely, which depends heavily on the operator’s experience and severely affects the working quality. In order to control the motion of the harvester precisely, it is essential to acquire its real-time motion information along the planting ridge. A low-cost method to obtain the motion information of the profiling tea harvester based on visual inertial odometry (VIO) was proposed, which was named as CYVIO. A tightly coupled VIO algorithm is established based on an RGB-D camera, which recognizes the tea ridge by optical flow estimation with sparse feature matching to adapt the VIO algorithm to the tea field. Based on obtaining the six degrees of freedom pose of the tea harvester, a velocity resolving algorithm that can adapt to the complex environment is developed. Finally, the validity and reliability of CYVIO are verified by comparison experiments between CYVIO and IMU, as well as other algorithms. The results show that the algorithm has a better effect on the tea plantation environment. When the motion velocity of the harvester is from 0.3 m/s to 0.7 m/s, the average error rates of the three ridges of experiments were 2.84 %, 3.67 %, and 3.18 %, respectively, which were low enough to meet the demand for precise control of the profiling tea harvester.
The use of line features to improve the localization accuracy of point-based visual inertial SLAM (VINS) is of increasing interest because of the additional constraints they can provide on the scene ...structure. It is found that although the introduction of line features improves the accuracy of relative position estimation in some scenes due to the additional constraints, the two constraints only achieve an equalization for the estimated relative position. Therefore, in some environments two constraints can reduce the accuracy of the single-feature constraint algorithm and also make the real-time performance of the system more challenging. To address such issues, in this paper, we design a generalized SLAM system with point-line features that can be applied to multiple metaverse scenarios. We first enhance the image frames used for feature extraction by eliminating motion blur frames through the fuzzy metric method and mutation modeling of velocity and rotation. Then we improve the traditional line detection model by short line fusion, uniform distribution of line features, and refinement of edge features to obtain high-quality line features for building SLAM. Finally, based on the application of point features and line features for different scenes, a point and line feature separation-union model is proposed. In addition, we design a transformation model for line-point features to enhance the processing of point-line features. Experimental results on EuRoc, TUM_VI, KITTI, PennCOSYVIO and our own recorded dataset prove that the method proposed in this paper realizes a generalized SLAM with point and line features applied to multi-scenes with good advantages.
Recently, micro electro-mechanical systems (MEMS) inertial sensors have found their way in various applications. These sensors are fairly low cost and easily available but their measurements are ...noisy and imprecise, which poses the necessity of calibration. In this paper, we present an approach to calibrate an inertial measurement unit (IMU) comprised of a low-cost tri-axial MEMS accelerometer and a gyroscope. As opposed to existing methods, our method is truly infield as it requires no external equipment and utilizes gravity signal as a stable reference. It only requires the sensor to be placed in approximate orientations, along with the application of simple rotations. This also offers easier and quicker calibration comparatively. We analyzed the method by performing experiments on two different IMUs: an in-house built IMU and a commercially calibrated IMU. We also calibrated the in-house built IMU using an aviation grade rate table for comparison. The results validate the calibration method as a useful low-cost IMU calibration scheme.
