A sensor fusion approach for localization with cumulative error elimination

Zhang, Feihu; Stähle, Hauke; Chen, Guang; Simon, Chao Chen Carsten; Buckl, Christian; Knoll, Alois

doi:10.1109/mfi.2012.6343009

Cited by 35 publications

(22 citation statements)

References 8 publications

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“…The main principle of GPS-IMU fusion is correcting accumulated errors of dead reckoning in intervals with absolute position readings [107]. In a GPS-IMU system, changes in position and orientation are measured by IMU, and this information is processed for localizing the robot with dead reckoning.…”

Section: A Gps-imu Fusionmentioning

confidence: 99%

“…The accuracy required for automated driving in urban scenes cannot be realized with the current GPS-IMU technology. Moreover, in dense urban environments, the accuracy drops further, and the GPS stops functioning from time to time because of tunnels [107] and high buildings. Even though GPS-IMU systems by themselves do not meet the performance requirements and cannot be utilized except high-level route planning, they are used for initial pose estimation in tandem with lidar and other sensors in state-ofthe-art localization systems [109].…”

Section: A Gps-imu Fusionmentioning

confidence: 99%

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A Survey of Autonomous Driving: Common Practices and Emerging Technologies

et al. 2020

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Automated driving systems (ADSs) promise a safe, comfortable and efficient driving experience. However, fatalities involving vehicles equipped with ADSs are on the rise. The full potential of ADSs cannot be realized unless the robustness of state-of-the-art improved further. This paper discusses unsolved problems and surveys the technical aspect of automated driving. Studies regarding present challenges, high-level system architectures, emerging methodologies and core functions: localization, mapping, perception, planning, and human machine interface, were thoroughly reviewed. Furthermore, the state-of-the-art was implemented on our own platform and various algorithms were compared in a real-world driving setting. The paper concludes with an overview of available datasets and tools for ADS

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Section: A Gps-imu Fusionmentioning

confidence: 99%

Section: A Gps-imu Fusionmentioning

confidence: 99%

A Survey of Autonomous Driving: Common Practices and Emerging Technologies

et al. 2020

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“…This problem can be overcome by correcting the estimated position using other sensors, to avoid accumulated drift and to provide global positioning. To address the low accuracy and interference issues in GPS as well as the cumulative errors related to IMU sensors, the authors in [26] proposed integrating the information from the GPS and IMU. To test the approach, the authors used a GPS/IMU system which provides GPS data, heading angle, and velocity of the vehicle at 10Hz.…”

Section: A Gps/imu Based Techniquesmentioning

confidence: 99%

A Survey of the State-of-the-Art Localization Techniques and Their Potentials for Autonomous Vehicle Applications

Kuutti

Fallah

Κατσαρός

et al. 2018

IEEE Internet Things J.

667

284

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For an autonomous vehicle to operate safely and effectively, an accurate and robust localisation system is essential. While there are a variety of vehicle localisation techniques in literature, there is a lack of effort in comparing these techniques and identifying their potentials and limitations for autonomous vehicle applications. Hence, this paper evaluates the state-of-the-art vehicle localisation techniques and investigates their applicability on autonomous vehicles. The analysis starts with discussing the techniques which merely use the information obtained from on-board vehicle sensors. It is shown that although some techniques can achieve the accuracy required for autonomous driving but suffer from the high cost of the sensors and also sensor performance limitations in different driving scenarios (e.g. cornering, intersections) and different environmental conditions (e.g. darkness, snow). The paper continues the analysis with considering the techniques which benefit from off-board information obtained from V2X communication channels, in addition to vehicle sensory information. The analysis shows that augmenting off-board information to sensory information has potential to design low-cost localisation systems with high accuracy and robustness however their performance depends on penetration rate of nearby connected vehicles or infrastructure and the quality of network service.

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“…The fusion techniques with vision sensors have under a meter accuracy; however, these techniques are vulnerable to illumination, the observation angle, the weather condition, and dynamic environments. The integrated GPS and IMU sensor localization technique has a relatively low accuracy of 7.2 m and error accumulation problem [ 38 ]. Another error-accumulation-free IMU-based localization method [ 21 ] has about a 5~10 m accuracy and estimation error deviations are large due to the signal noise.…”

Section: Experimental Validationmentioning

confidence: 99%

IMU-Based Virtual Road Profile Sensor for Vehicle Localization

Gim

Ahn

2018

Sensors

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A road profile can be a good reference feature for vehicle localization when a Global Positioning System signal is unavailable. However, cost effective and compact devices measuring road profiles are not available for production vehicles. This paper presents a longitudinal road profile estimation method as a virtual sensor for vehicle localization without using bulky and expensive sensor systems. An inertial measurement unit installed in the vehicle provides filtered signals of the vehicle’s responses to the longitudinal road profile. A disturbance observer was designed to extract the characteristic features of the road profile from the signals measured by the inertial measurement unit. Design synthesis based on a Kalman filter was used for the observer design. A nonlinear damper is explicitly considered to improve the estimation accuracy. Virtual measurement signals are introduced for observability. The suggested methodology estimates the road profile that is sufficiently accurate for localization. Based on the estimated longitudinal road profile, we generated spectrogram plots as the features for localization. The localization is realized by matching the spectrogram plot with pre-indexed plots. The localization using the estimated road profile shows a few meters accuracy, suggesting a possible road profile estimation method as an alternative sensor for vehicle localization.

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A sensor fusion approach for localization with cumulative error elimination

Cited by 35 publications

References 8 publications

A Survey of Autonomous Driving: Common Practices and Emerging Technologies

A Survey of Autonomous Driving: Common Practices and Emerging Technologies

A Survey of the State-of-the-Art Localization Techniques and Their Potentials for Autonomous Vehicle Applications

IMU-Based Virtual Road Profile Sensor for Vehicle Localization

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