scholarly journals IMU-Aided High-Frequency Lidar Odometry for Autonomous Driving

2019 ◽  
Vol 9 (7) ◽  
pp. 1506 ◽  
Author(s):  
Hanzhang Xue ◽  
Hao Fu ◽  
Bin Dai
Keyword(s):  
High Frequency ◽  
Inertial Measurement Unit ◽  
Autonomous Driving ◽  
Initial Guess ◽  
Measurement Unit ◽  
Unmanned Ground Vehicle ◽  
Ground Vehicle ◽  
Diverse Environment ◽  
Novel Method ◽  
Localization Information

For autonomous driving, it is important to obtain precise and high-frequency localization information. This paper proposes a novel method in which the Inertial Measurement Unit (IMU), wheel encoder, and lidar odometry are utilized together to estimate the ego-motion of an unmanned ground vehicle. The IMU is fused with the wheel encoder to obtain the motion prior, and it is involved in three levels of the lidar odometry: Firstly, we use the IMU information to rectify the intra-frame distortion of the lidar scan, which is caused by the vehicle’s own movement; secondly, the IMU provides a better initial guess for the lidar odometry; and thirdly, the IMU is fused with the lidar odometry in an Extended Kalman filter framework. In addition, an efficient method for hand–eye calibration between the IMU and the lidar is proposed. To evaluate the performance of our method, extensive experiments are performed and our system can output stable, accurate, and high-frequency localization results in diverse environment without any prior information.

Comparison of UGV Position Estimation Equipped With GNSS-RTK and GPS Using EKF

2020 ◽  
Author(s):  
Hesham Ismail ◽  
Thani Althani ◽  
Mohammed Minhas Anzil ◽  
Prashanth Subramaniam
Keyword(s):  
Extreme Temperature ◽  
Processing Technique ◽  
Position Estimation ◽  
Image Processing Technique ◽  
Measurement Unit ◽  
Unmanned Ground Vehicle ◽  
Camera System ◽  
Ground Vehicle ◽  
Positioning Device ◽  
Global Navigation Satellite

Abstract Site assessments for bifacial Photovoltaic (PV) installation are quite challenging to conduct manually due to the area size and the extreme temperature conditions at desert sites. We designed and built an autonomous Unmanned Ground Vehicle (UGV) fitted with a Global Navigation Satellite Network-System Real-Time Kinematic (GNSS-RTK) positioning device, an Inertial Measurement Unit (IMU), encoder to improve and aid site assessments in desert condition. Sandy terrains deserts are challenging for UGV’s because they increase the likelihood of wheel slippage due to reduced traction. Sensor details such as IMU, GNSS-RTK, and encoder should be taken into consideration to account for the errors that the desert terrains pose. This study compared the Extended Kalman Filter (EKF) for standard GPS & GNSS-RTK to verify which performs better for the UGV’s position estimation. The estimated UGV’s position from the kinematics model and EKF are validated using a drone camera system that uses an image processing technique to verify the UGV’s position with the help of the visible reference cones. Throughout the experiments, the GNSS-RTK performed better than GPS. Also, the EKF performed as well as the GNSS-RTK by trusting it more than the encoder/gyroscope reading.

scholarly journals Modeling and Calculation of Dent Based on Pipeline Bending Strain

2016 ◽  
Vol 2016 ◽  
pp. 1-8 ◽  
Author(s):  
Qingshan Feng ◽  
Rui Li ◽  
Hong Zhang
Keyword(s):  
Inertial Measurement Unit ◽  
Oil And Gas ◽  
Measurement Unit ◽  
Mapping Data ◽  
Long Distance ◽  
Bending Strain ◽  
Strain Data ◽  
Integrity Management ◽  
Novel Method ◽  
The Mean

The bending strain of long-distance oil and gas pipelines can be calculated by the in-line inspection tool which used inertial measurement unit (IMU). The bending strain is used to evaluate the strain and displacement of the pipeline. During the bending strain inspection, the dent existing in the pipeline can affect the bending strain data as well. This paper presents a novel method to model and calculate the pipeline dent based on the bending strain. The technique takes inertial mapping data from in-line inspection and calculates depth of dent in the pipeline using Bayesian statistical theory and neural network. To verify accuracy of the proposed method, an in-line inspection tool is used to inspect pipeline to gather data. The calculation of dent shows the method is accurate for the dent, and the mean relative error is 2.44%. The new method provides not only strain of the pipeline dent but also the depth of dent. It is more benefit for integrity management of pipeline for the safety of the pipeline.

Control Limitations on Static Unstable Airframe Using Two-Loop Acceleration Autopilot

2011 ◽  
Vol 383-390 ◽  
pp. 4385-4390
Author(s):  
Jiang Wang ◽  
De Fu Lin ◽  
Jun Fang Fan
Keyword(s):  
High Frequency ◽  
Inertial Measurement Unit ◽  
Feedback Loop ◽  
Measurement Unit ◽  
Flight Performance ◽  
Actuator Dynamics ◽  
Rate Feedback ◽  
Stabilization Condition ◽  
Lever Effect ◽  
Loop Acceleration

An analysis for controlling a static-unstable tactical missile using two-loop acceleration autopilot was detailed. The rate feedback loop was firstly presented. The equivalent actuator dynamics was introduced and examined. Thus an overall stabilization condition combined with both low and high frequency cases was proposed. The lever effect led by inertial measurement unit was of benefit to a great controllable range. The results show that the autopilot control capacity is dominated by actuator bandwidth, and a compromise should be determined between the flight performance and the actuator requirement for a static unstable tactical missile.

scholarly journals An Optimized Complementary Filter For An Inertial Measurement Unit Contain MPU6050 Sensor

2019 ◽  
Vol 15 (2) ◽  
pp. 71-77
Author(s):  
Ahmed Albaghdadi ◽  
Abduladhem Ali
Keyword(s):  
High Frequency ◽  
Inertial Measurement Unit ◽  
Vertical Axis ◽  
Measurement Unit ◽  
Second Step ◽  
Gray Wolf ◽  
Mathematical Relationship ◽  
Inertial Measurement ◽  
Complementary Filter ◽  
Gyroscope Sensor

It can be said that the system of sensing the tilt angle and speed of a multi-rotor copter come in the first rank among all the other sensors on the multi-rotor copters and all other planes due to its important roles for stabilization. The MPU6050 sensor is one of the most popular sensors in this field. It has an embedded 3-axis accelerometer and a 3-axis gyroscope. It is a simple sensor in dealing with it and extracting accurate data. Everything changes when this sensor is placed on the plane. It becomes very complicated to deal with it due to vibration of the motors on the multirotor copter. In this study, two main problems were diagnosed was solved that appear in most sensors when they are applied to a high-frequency vibrating environment. The first problem is how to get a precise angle of the sensor despite the presence of vibration. The second problem is how to overcome the errors that appear when the multirotor copter revolves around its vertical axis during the tilting in either direction x or y or both. The first problem was solved in two steps. The first step involves mixing data of the gyroscope sensor with the data of auxetometer sensor by a mathematical equation based on optimized complementary filter using gray wolf optimization algorithm GWO. The second step involves designing a suitable FIR filter for data. The second problem was solved by finding a non-linear mathematical relationship between the angles of the copter in both X and Y directions, and the rotation around the vertical axis of multirotor copter frame.

scholarly journals A Precise and Robust Segmentation-Based Lidar Localization System for Automated Urban Driving

2019 ◽  
Vol 11 (11) ◽  
pp. 1348 ◽  
Author(s):  
Hang Liu ◽  
Qin Ye ◽  
Hairui Wang ◽  
Liang Chen ◽  
Jian Yang
Keyword(s):  
Real Time ◽  
Semantic Segmentation ◽  
Autonomous Driving ◽  
Unmanned Vehicles ◽  
Measurement Unit ◽  
Integrated Navigation ◽  
Current Frame ◽  
Localization System ◽  
Robust Segmentation ◽  
Localization Information

Real-time and high-precision localization information is vital for many modules of unmanned vehicles. At present, a high-cost RTK (Real Time Kinematic) and IMU (Integrated Measurement Unit) integrated navigation system is often used, but its accuracy cannot meet the requirements and even fails in many scenes. In order to reduce the costs and improve the localization accuracy and stability, we propose a precise and robust segmentation-based Lidar (Light Detection and Ranging) localization system aided with MEMS (Micro-Electro-Mechanical System) IMU and designed for high level autonomous driving. Firstly, we extracted features from the online frame using a series of proposed efficient low-level semantic segmentation-based multiple types feature extraction algorithms, including ground, road-curb, edge, and surface. Next, we matched the adjacent frames in Lidar odometry module and matched the current frame with the dynamically loaded pre-build feature point cloud map in Lidar localization module based on the extracted features to precisely estimate the 6DoF (Degree of Freedom) pose, through the proposed priori information considered category matching algorithm and multi-group-step L-M (Levenberg-Marquardt) optimization algorithm. Finally, the lidar localization results were fused with MEMS IMU data through a state-error Kalman filter to produce smoother and more accurate localization information at a high frequency of 200Hz. The proposed localization system can achieve 3~5 cm in position and 0.05~0.1° in orientation RMS (Root Mean Square) accuracy and outperform previous state-of-the-art systems. The robustness and adaptability have been verified with localization testing data more than 1000 Km in various challenging scenes, including congested urban roads, narrow tunnels, textureless highways, and rain-like harsh weather.

Design and Realization of an All-terrain Unmanned Ground Vehicle

ROBOT ◽  
2013 ◽  
Vol 35 (6) ◽  
pp. 657 ◽  
Author(s):  
Taoyi ZHANG ◽  
Tianmiao WANG ◽  
Yao WU ◽  
Qiteng ZHAO
Keyword(s):  
Unmanned Ground Vehicle ◽  
Ground Vehicle
Sign in / Sign up

Export Citation Format

Share Document