scholarly journals Ego-Lane Index Estimation Based on Lane-Level Map and LiDAR Road Boundary Detection

Sensors ◽  
2021 ◽  
Vol 21 (21) ◽  
pp. 7118
Author(s):  
Baoguo Yu ◽  
Hongjuan Zhang ◽  
Wenzhuo Li ◽  
Chuang Qian ◽  
Bijun Li ◽  
...  
Keyword(s):  
Single Point ◽  
Dead Reckoning ◽  
Boundary Detection ◽  
Position Estimation ◽  
Intelligent Vehicles ◽  
Measurement Unit ◽  
Distance Measurements ◽  
Importance Weighting ◽  
Lidar Measurements ◽  
Index Estimation

Correct ego-lane index estimation is essential for lane change and decision making for intelligent vehicles, especially in global navigation satellite system (GNSS)-challenged environments. To achieve this, we propose an ego-lane index estimation approach in an urban scenario based on particle filter (PF). The particles are initialized and propagated by dead reckoning with inertial measurement unit (IMU) and odometry. A lane-level map is used to navigate the particles taking advantage of topologic and geometric information of lanes. GNSS single-point positioning (SPP) can provide position estimation with meter-level accuracy in urban environments, which can limit drift introduced by dead reckoning for updating the weight of each particle. Light detection and ranging (LiDAR) is a common sensor in an intelligent vehicle. A LiDAR-based road boundary detection method provides distance measurements from the vehicle to the left/right road boundaries, which provides a measurement for importance weighting. However, the high precision of the LiDAR measurements may put a tight constraint on the distribution of particles, which can lead to particle degeneration with sparse particle sets. To deal with this problem, we propose a novel step that shifts particles laterally based on LiDAR measurements instead of importance weighting in the traditional PF scheme. We tested our methods on an urban expressway at a low traffic volume period to ensure road boundaries can be detected by LiDAR measurements at most time steps. Experimental results prove that our improved PF scheme can correctly estimate ego-lane index at all time steps, while the traditional PF scheme produces wrong estimations at some time steps.

scholarly journals A Sensor Fusion Framework for Indoor Localization Using Smartphone Sensors and Wi-Fi RSSI Measurements

2019 ◽  
Vol 9 (20) ◽  
pp. 4379 ◽  
Author(s):  
Alwin Poulose ◽  
Jihun Kim ◽  
Dong Seog Han
Keyword(s):  
Sensor Fusion ◽  
Indoor Localization ◽  
Dead Reckoning ◽  
Position Estimation ◽  
Sensor Data ◽  
Measurement Unit ◽  
Localization Error ◽  
Positioning Errors ◽  
Indoor Scenarios ◽  
Fusion Framework

Sensor fusion frameworks for indoor localization are developed with the specific goal of reducing positioning errors. Although many conventional localization frameworks without fusion have been improved to reduce positioning error, sensor fusion frameworks generally provide a further improvement in positioning accuracy. In this paper, we propose a sensor fusion framework for indoor localization using the smartphone inertial measurement unit (IMU) sensor data and Wi-Fi received signal strength indication (RSSI) measurements. The proposed sensor fusion framework uses location fingerprinting and trilateration for Wi-Fi positioning. Additionally, a pedestrian dead reckoning (PDR) algorithm is used for position estimation in indoor scenarios. The proposed framework achieves a maximum of 1.17 m localization error for the rectangular motion of a pedestrian and a maximum of 0.44 m localization error for linear motion.

scholarly journals Pedestrian navigation system based on the inertial measurement unit sensor for outdoor and indoor environments

2020 ◽  
Vol 9 (1) ◽  
pp. 7-13
Author(s):  
Marcin Uradzinski ◽  
Hang Guo
Keyword(s):  
Inertial Measurement Unit ◽  
Angular Rate ◽  
Dead Reckoning ◽  
Step Length ◽  
Position Estimation ◽  
Outdoor Environment ◽  
Measurement Unit ◽  
Indoor Environments ◽  
Inertial Measurement ◽  
Step Detection

Abstract. With the continuous improvement of the hardware level of the inertial measurement unit (IMU), indoor pedestrian dead reckoning (PDR) using an inertial device has been paid more and more attention. Typical PDR system position estimation is based on acceleration obtained from accelerometers to measure the step count, estimate step length and generate the position with the heading received from angular sensors (magnetometers and gyroscopes). Unfortunately, collected signals are very responsive to the alignment of sensor devices, built-in instrumental errors and distortions from the surrounding environment. In our work, a pedestrian positioning method using step detection based on a shoe-mounted inertial unit is arranged and put to the test, and the final results are analyzed. The extended Kalman filter (EKF) provides estimation of the errors which are acquired by the XSENS IMU sensor biases. The EKF is revised with acceleration and angular rate computations by the ZUPT (zero velocity update) and ZARU (zero angular rate update) algorithms. The step detection associated with these two solutions is the perfect choice to calculate the current position and distance walked and to estimate the IMU sensors' collected errors by using EKF. The test with a shoe-mounted IMU device was performed and analyzed in order to check the performance of the recommended method. The combined PDR final results were compared to GPS/Beidou postprocessing kinematic results (outdoor environment) and to a real route which was prepared and calculated for an indoor environment. After the comparison, the results show that the accuracy of the regular-speed walking under ZUPT and ZARU combination in the case of outdoor positioning did not go beyond 0.19 m (SD) and for indoor positioning accuracy did not exceed 0.22 m (SD). The authors are conscious that built-in drift errors coming from accelerometers and gyroscopes, as well as the final position obtained by XSENS IMU, are only stable for a short time period. Based on this consideration, our future work will be focused on supporting the methods presented with radio technologies (WiFi) or image-based solutions to correct all IMU imperfections.

scholarly journals Indoor Positioning Based on Pedestrian Dead Reckoning and Magnetic Field Matching for Smartphones

Sensors ◽  
2018 ◽  
Vol 18 (12) ◽  
pp. 4142 ◽  
Author(s):  
Jian Kuang ◽  
Xiaoji Niu ◽  
Peng Zhang ◽  
Xingeng Chen
Keyword(s):  
Magnetic Field ◽  
Estimation Error ◽  
Magnetic Field Gradient ◽  
Single Point ◽  
Dead Reckoning ◽  
Position Estimation ◽  
Shopping Mall ◽  
Mm Algorithm ◽  
Field Matching ◽  
Pedestrian Dead Reckoning

This paper presents an ambient magnetic field map-based matching (MM) positioning algorithm for smartphones in an indoor environment. To improve the low distinguishability of a magnetic field fingerprint at a single point, a magnetic field sequence (MFS) combined with the measured trajectory contour coming from pedestrian dead-reckoning (PDR) is used for MM. Based on the fast approximation of magnetic field gradient, a Gauss-Newton iterative (GNI) method is used to find a rigid transformation that optimally aligns the measured MFS with a reference MFS coming from the magnetic field map. Then, the position of the reference MFS is used to control the position drift error of the inertial navigation system (INS) based PDR by an extended Kalman filter (EKF) and to further improve the accuracy of the trajectory contour. Finally, we conduct several experiments to evaluate the navigation performance of the proposed MM algorithm. The test results show that the position estimation error of the MM algorithm is 0.64 m (RMS) in an office building environment, 1.87 m (RMS) in a typical lobby environment, and 2.34 m (RMS) in a shopping mall environment.

scholarly journals Pedestrian Stride Length Estimation from IMU Measurements and ANN Based Algorithm

2017 ◽  
Vol 2017 ◽  
pp. 1-10 ◽  
Author(s):  
Haifeng Xing ◽  
Jinglong Li ◽  
Bo Hou ◽  
Yongjian Zhang ◽  
Meifeng Guo
Keyword(s):  
Stride Length ◽  
Nonlinear Models ◽  
Back Propagation ◽  
Dead Reckoning ◽  
Estimation Algorithm ◽  
Position Estimation ◽  
Superior Performance ◽  
Measurement Unit ◽  
Neural Net ◽  
Length Estimation

Pedestrian dead reckoning (PDR) can be used for continuous position estimation when satellite or other radio signals are not available, and the accuracy of the stride length measurement is important. Current stride length estimation algorithms, including linear and nonlinear models, consider a few variable factors, and some rely on high precision and high cost equipment. This paper puts forward a stride length estimation algorithm based on a back propagation artificial neural network (BP-ANN), using a consumer-grade inertial measurement unit (IMU); it then discusses various factors in the algorithm. The experimental results indicate that the error of the proposed algorithm in estimating the stride length is approximately 2%, which is smaller than that of the frequency and nonlinear models. Compared with the latter two models, the proposed algorithm does not need to determine individual parameters in advance if the trained neural net is effective. It can, thus, be concluded that this algorithm shows superior performance in estimating pedestrian stride length.

scholarly journals Sensor Modeling for Underwater Localization Using a Particle Filter

Sensors ◽  
2021 ◽  
Vol 21 (4) ◽  
pp. 1549
Author(s):  
Humberto Martínez-Barberá ◽  
Pablo Bernal-Polo ◽  
David Herrero-Pérez
Keyword(s):  
Sequential Monte Carlo ◽  
Sensory Information ◽  
Dead Reckoning ◽  
Underwater Vehicle ◽  
Position Estimation ◽  
Sources Of Uncertainty ◽  
Sensor Signals ◽  
Underwater Navigation ◽  
Underwater Localization ◽  
Sensor Modeling

This paper presents a framework for processing, modeling, and fusing underwater sensor signals to provide a reliable perception for underwater localization in structured environments. Submerged sensory information is often affected by diverse sources of uncertainty that can deteriorate the positioning and tracking. By adopting uncertain modeling and multi-sensor fusion techniques, the framework can maintain a coherent representation of the environment, filtering outliers, inconsistencies in sequential observations, and useless information for positioning purposes. We evaluate the framework using cameras and range sensors for modeling uncertain features that represent the environment around the vehicle. We locate the underwater vehicle using a Sequential Monte Carlo (SMC) method initialized from the GPS location obtained on the surface. The experimental results show that the framework provides a reliable environment representation during the underwater navigation to the localization system in real-world scenarios. Besides, they evaluate the improvement of localization compared to the position estimation using reliable dead-reckoning systems.

scholarly journals Many Ways Lead to the Goal—Possibilities of Autonomous and Infrastructure-Based Indoor Positioning

Electronics ◽  
2021 ◽  
Vol 10 (4) ◽  
pp. 397
Author(s):  
Hossein Shoushtari ◽  
Thomas Willemsen ◽  
Harald Sternberg
Keyword(s):  
Mobile Networks ◽  
Dead Reckoning ◽  
Simulated Data ◽  
Minimum Cost ◽  
Cost Effective ◽  
Satellite System ◽  
Position Estimation ◽  
Robust Solution ◽  
Position Data ◽  
Global Navigation Satellite

There are many ways to navigate in Global Navigation Satellite System-(GNSS) shaded areas. Reliable indoor pedestrian navigation has been a central aim of technology researchers in recent years; however, there still exist open challenges requiring re-examination and evaluation. In this paper, a novel dataset is used to evaluate common approaches for autonomous and infrastructure-based positioning methods. The autonomous variant is the most cost-effective realization; however, realizations using the real test data demonstrate that the use of only autonomous solutions cannot always provide a robust solution. Therefore, correction through the use of infrastructure-based position estimation based on smartphone technology is discussed. This approach invokes the minimum cost when using existing infrastructure, whereby Pedestrian Dead Reckoning (PDR) forms the basis of the autonomous position estimation. Realizations with Particle Filters (PF) and a topological approach are presented and discussed. Floor plans and routing graphs are used, in this case, to support PDR positioning. The results show that the positioning model loses stability after a given period of time. Fifth Generation (5G) mobile networks can enable this feature, as well as a massive number of use-cases, which would benefit from user position data. Therefore, a fusion concept of PDR and 5G is presented, the benefit of which is demonstrated using the simulated data. Subsequently, the first implementation of PDR with 5G positioning using PF is carried out.

scholarly journals Integration of GPS, Monocular Vision, and High Definition (HD) Map for Accurate Vehicle Localization

Sensors ◽  
2018 ◽  
Vol 18 (10) ◽  
pp. 3270 ◽  
Author(s):  
Hao Cai ◽  
Zhaozheng Hu ◽  
Gang Huang ◽  
Dunyao Zhu ◽  
Xiaocong Su
Keyword(s):  
Kalman Filter ◽  
Low Cost ◽  
Kinematic Model ◽  
Intelligent Vehicles ◽  
Monocular Vision ◽  
Measurement Unit ◽  
High Definition ◽  
Position Information ◽  
Vehicle Localization ◽  
Lateral Distance

Self-localization is a crucial task for intelligent vehicles. Existing localization methods usually require high-cost IMU (Inertial Measurement Unit) or expensive LiDAR sensors (e.g., Velodyne HDL-64E). In this paper, we propose a low-cost yet accurate localization solution by using a custom-level GPS receiver and a low-cost camera with the support of HD map. Unlike existing HD map-based methods, which usually requires unique landmarks within the sensed range, the proposed method utilizes common lane lines for vehicle localization by using Kalman filter to fuse the GPS, monocular vision, and HD map for more accurate vehicle localization. In the Kalman filter framework, the observations consist of two parts. One is the raw GPS coordinate. The other is the lateral distance between the vehicle and the lane, which is computed from the monocular camera. The HD map plays the role of providing reference position information and correlating the local lateral distance from the vision and the GPS coordinates so as to formulate a linear Kalman filter. In the prediction step, we propose using a data-driven motion model rather than a Kinematic model, which is more adaptive and flexible. The proposed method has been tested with both simulation data and real data collected in the field. The results demonstrate that the localization errors from the proposed method are less than half or even one-third of the original GPS positioning errors by using low cost sensors with HD map support. Experimental results also demonstrate that the integration of the proposed method into existing ones can greatly enhance the localization results.

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 Comparative Analysis between Error-State and Full-State Error Estimation for KF-Based IMU/GNSS Integration against IMU Faults

Sensors ◽  
2019 ◽  
Vol 19 (22) ◽  
pp. 4912 ◽  
Author(s):  
Wei Liu ◽  
Dan Song ◽  
Zhipeng Wang ◽  
Kun Fang
Keyword(s):  
Satellite System ◽  
Position Estimation ◽  
Measurement Unit ◽  
Multisensor Systems ◽  
System Integrity ◽  
Full State ◽  
Tightly Coupled ◽  
Integrity Assurance ◽  
Error State ◽  
State Error

Considering the inertial measurement unit (IMU) faults risk of an unmanned aerial vehicle (UAV), this paper provides an analysis of the error overboundings of position estimation in a tightly coupled IMU/global navigation satellite system (GNSS) integrated architecture under the IMU fault conditions using an error-state EKF-based approach and provides a comparison to a recently published EKF-based full state method. Simulation results show that both the error overboundings of the error-state and full-state EKFs can fit the state error against the IMU faults, but the error-state EKF is more suitable for UAV navigation system integrity assurance due to its higher calculation effciency. This study will be extended to the integrity monitoring of multisensor systems.

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