scholarly journals CKF-Based Visual Inertial Odometry for Long-Term Trajectory Operations

2020 ◽  
Vol 2020 ◽  
pp. 1-14
Author(s):  
Trung Nguyen ◽  
George K. I. Mann ◽  
Andrew Vardy ◽  
Raymond G. Gosine
Keyword(s):  
Low Cost ◽  
Estimation Error ◽  
Optimization Procedure ◽  
Single Measurement ◽  
Cubature Kalman Filter ◽  
Graph Optimization ◽  
Error Accumulation ◽  
Entire Trajectory ◽  
Pose Graph Optimization

The estimation error accumulation in the conventional visual inertial odometry (VIO) generally forbids accurate long-term operations. Some advanced techniques such as global pose graph optimization and loop closure demand relatively high computation and processing time to execute the optimization procedure for the entire trajectory and may not be feasible to be implemented in a low-cost robotic platform. In an attempt to allow the VIO to operate for a longer duration without either using or generating a map, this paper develops iterated cubature Kalman filter for VIO application that performs multiple corrections on a single measurement to optimize the current filter state and covariance during the measurement update. The optimization process is terminated using the maximum likelihood estimate based criteria. For comparison, this paper also develops a second solution to integrate VIO estimation with ranging measurements. The wireless communications between the vehicle and multiple beacons produce the ranging measurements and help to bound the accumulative errors. Experiments utilize publicly available dataset for validation, and a rigorous comparison between the two solutions is presented to determine the application scenario of each solution.

scholarly journals A Robust EMD-Based RVFL Network Fusion Algorithm for Low-Cost GPS/INS Integrated System

2019 ◽  
Vol 2019 ◽  
pp. 1-12
Author(s):  
Da Liu ◽  
Shufang Zhang ◽  
Jingbo Zhang
Keyword(s):  
Inertial Sensors ◽  
Low Cost ◽  
Integrated System ◽  
Fusion Algorithm ◽  
Functional Link ◽  
Mode Decomposition ◽  
Error Accumulation ◽  
Lower Complexity ◽  
High Level

Global positioning system (GPS) and inertial navigation system (INS) are commonly combined to overcome disadvantages of each and constitute an integrated system that realizes long-term precision. However, the performance of the integrated system deteriorates on which GPS is unavailable. Especially when low-cost inertial sensors based on the microelectromechanical system (MEMS) are used, performance of the integrated system degrades severely over time. In this study, in order to minimize the adverse impact of high-level stochastic noise from low-cost MEMS sensors, denoising technology based on empirical mode decomposition (EMD) is employed to improve signal quality before navigation solution by which significant improvement of removing noise is achieved. Moreover, a random vector functional link (RVFL) network-based fusion algorithm is presented to estimate and compensate position error during GPS outage such that error accumulation is suppressed quickly when INS is working standalone. Performance of the proposed approach is evaluated by experimental results. It is indicated from comparison that the proposed algorithm takes advantages such as better accuracy and lower complexity and is more robust than the commonly reported methods and is more appropriate for real-time and low-cost application.

scholarly journals Unsupervised Monocular Visual-inertial Odometry Network

2020 ◽  
Author(s):  
Peng Wei ◽  
Guoliang Hua ◽  
Weibo Huang ◽  
Fanyang Meng ◽  
Hong Liu
Keyword(s):  
Low Cost ◽  
Depth Map ◽  
Ground Truth ◽  
Optimization Strategy ◽  
Inherent Limitation ◽  
Geometric Consistency ◽  
Motion Features ◽  
Error Accumulation ◽  
Inertial Measurements

Recently, unsupervised methods for monocular visual odometry (VO), with no need for quantities of expensive labeled ground truth, have attracted much attention. However, these methods are inadequate for long-term odometry task, due to the inherent limitation of only using monocular visual data and the inability to handle the error accumulation problem. By utilizing supplemental low-cost inertial measurements, and exploiting the multi-view geometric constraint and sequential constraint, an unsupervised visual-inertial odometry framework (UnVIO) is proposed in this paper. Our method is able to predict the per-frame depth map, as well as extracting and self-adaptively fusing visual-inertial motion features from image-IMU stream to achieve long-term odometry task. A novel sliding window optimization strategy, which consists of an intra-window and an inter-window optimization, is introduced for overcoming the error accumulation and scale ambiguity problem. The intra-window optimization restrains the geometric inferences within the window through checking the photometric consistency. And the inter-window optimization checks the 3D geometric consistency and trajectory consistency among predictions of separate windows. Extensive experiments have been conducted on KITTI and Malaga datasets to demonstrate the superiority of UnVIO over other state-of-the-art VO / VIO methods. The codes are open-source.

scholarly journals Real-Time Vehicle Positioning and Mapping Using Graph Optimization

Sensors ◽  
2021 ◽  
Vol 21 (8) ◽  
pp. 2815
Author(s):  
Anweshan Das ◽  
Jos Elfring ◽  
Gijs Dubbelman
Keyword(s):  
Real Time ◽  
Rural Areas ◽  
Low Cost ◽  
Ground Truth ◽  
Visual Odometry ◽  
Graph Optimization ◽  
Urban And Rural Areas ◽  
Vehicle Positioning ◽  
Rate Sensor ◽  
Pose Graph Optimization

In this work, we propose and evaluate a pose-graph optimization-based real-time multi-sensor fusion framework for vehicle positioning using low-cost automotive-grade sensors. Pose-graphs can model multiple absolute and relative vehicle positioning sensor measurements and can be optimized using nonlinear techniques. We model pose-graphs using measurements from a precise stereo camera-based visual odometry system, a robust odometry system using the in-vehicle velocity and yaw-rate sensor, and an automotive-grade GNSS receiver. Our evaluation is based on a dataset with 180 km of vehicle trajectories recorded in highway, urban, and rural areas, accompanied by postprocessed Real-Time Kinematic GNSS as ground truth. We compare the architecture’s performance with (i) vehicle odometry and GNSS fusion and (ii) stereo visual odometry, vehicle odometry, and GNSS fusion; for offline and real-time optimization strategies. The results exhibit a 20.86% reduction in the localization error’s standard deviation and a significant reduction in outliers when compared with automotive-grade GNSS receivers.

Fine Particle Mass Monitoring with Low-Cost Sensors: Corrections and Long-Term Performance Evaluation

2019 ◽  
Author(s):  
Carl Malings ◽  
Rebecca Tanzer ◽  
Aliaksei Hauryliuk ◽  
Provat K. Saha ◽  
Allen L. Robinson ◽  
...  
Keyword(s):  
Performance Evaluation ◽  
Fine Particle ◽  
Low Cost ◽  
Particle Mass ◽  
Long Term Performance ◽  
Term Performance

A Pose-Graph Optimization tool for MATLAB

2018 ◽  
Author(s):  
João Carlos Virgolino Soares ◽  
Marco Antonio Meggiolaro
Keyword(s):  
Graph Optimization ◽  
Pose Graph Optimization

scholarly journals Wittichenite semiconductor of Cu3BiS3 films for efficient hydrogen evolution from solar driven photoelectrochemical water splitting

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Dingwang Huang ◽  
Lintao Li ◽  
Kang Wang ◽  
Yan Li ◽  
Kuang Feng ◽  
...  
Keyword(s):  
Hydrogen Evolution ◽  
Water Splitting ◽  
Low Cost ◽  
Solar Hydrogen ◽  
Onset Potential ◽  
Term Stability ◽  
Long Term Stability ◽  
Solar Water Splitting ◽  
Current Densities

AbstractA highly efficient, low-cost and environmentally friendly photocathode with long-term stability is the goal of practical solar hydrogen evolution applications. Here, we found that the Cu3BiS3 film-based photocathode meets the abovementioned requirements. The Cu3BiS3-based photocathode presents a remarkable onset potential over 0.9 VRHE with excellent photoelectrochemical current densities (~7 mA/cm2 under 0 VRHE) and appreciable 10-hour long-term stability in neutral water solutions. This high onset potential of the Cu3BiS3-based photocathode directly results in a good unbiased operating photocurrent of ~1.6 mA/cm2 assisted by the BiVO4 photoanode. A tandem device of Cu3BiS3-BiVO4 with an unbiased solar-to-hydrogen conversion efficiency of 2.04% is presented. This tandem device also presents high stability over 20 hours. Ultimately, a 5 × 5 cm2 large Cu3BiS3-BiVO4 tandem device module is fabricated for standalone overall solar water splitting with a long-term stability of 60 hours.

scholarly journals A Low-Cost MEMS Missile-Borne Compound Rotation Modulation Scheme

Sensors ◽  
2021 ◽  
Vol 21 (14) ◽  
pp. 4910
Author(s):  
Xiaoqiao Yuan ◽  
Jie Li ◽  
Xi Zhang ◽  
Kaiqiang Feng ◽  
Xiaokai Wei ◽  
...  
Keyword(s):  
Rotation Axis ◽  
Low Cost ◽  
Roll Angle ◽  
Modulation Scheme ◽  
Navigation Systems ◽  
Rotation Scheme ◽  
Compound Rotation ◽  
Error Accumulation ◽  
Axis Rotation ◽  
Single Axis Rotation

Rotation modulation (RM) has been widely used in navigation systems to significantly improve the navigation accuracy of inertial navigation systems (INSs). However, the traditional single-axis rotation modulation cannot achieve the modulation of all the constant errors in the three directions; thus, it is not suitable for application in highly dynamic environments due to requirements for high precision in missiles. Aiming at the problems of error accumulation and divergence in the direction of rotation axis existing in the traditional single-axis rotation modulation, a novel rotation scheme is proposed. Firstly, the error propagation principle of the new rotation modulation scheme is analyzed. Secondly, the condition of realizing the error modulation with constant error is discussed. Finally, the original rotation modulation navigation algorithm is optimized for the new rotation modulation scheme. The experiment and simulation results show that the new rotation scheme can effectively modulate the error divergence of roll angle and improve the accuracy of roll angle by two orders of magnitude.

scholarly journals RTLIO: Real-Time LiDAR-Inertial Odometry and Mapping for UAVs

Sensors ◽  
2021 ◽  
Vol 21 (12) ◽  
pp. 3955
Author(s):  
Jung-Cheng Yang ◽  
Chun-Jung Lin ◽  
Bing-Yuan You ◽  
Yin-Long Yan ◽  
Teng-Hu Cheng
Keyword(s):  
Feedback Control ◽  
Real Time ◽  
High Frequency ◽  
Indoor Environment ◽  
Indoor Localization ◽  
Sampling Rate ◽  
Outdoor Environment ◽  
Position Accuracy ◽  
Graph Optimization ◽  
Pose Graph Optimization

Most UAVs rely on GPS for localization in an outdoor environment. However, in GPS-denied environment, other sources of localization are required for UAVs to conduct feedback control and navigation. LiDAR has been used for indoor localization, but the sampling rate is usually too low for feedback control of UAVs. To compensate this drawback, IMU sensors are usually fused to generate high-frequency odometry, with only few extra computation resources. To achieve this goal, a real-time LiDAR inertial odometer system (RTLIO) is developed in this work to generate high-precision and high-frequency odometry for the feedback control of UAVs in an indoor environment, and this is achieved by solving cost functions that consist of the LiDAR and IMU residuals. Compared to the traditional LIO approach, the initialization process of the developed RTLIO can be achieved, even when the device is stationary. To further reduce the accumulated pose errors, loop closure and pose-graph optimization are also developed in RTLIO. To demonstrate the efficacy of the developed RTLIO, experiments with long-range trajectory are conducted, and the results indicate that the RTLIO can outperform LIO with a smaller drift. Experiments with odometry benchmark dataset (i.e., KITTI) are also conducted to compare the performance with other methods, and the results show that the RTLIO can outperform ALOAM and LOAM in terms of exhibiting a smaller time delay and greater position accuracy.

Sign in / Sign up

Export Citation Format

Share Document