LiDAR-Based Sensor Fusion SLAM and Localization for Autonomous Driving Vehicles in Complex Scenarios

Dai, Kai; Sun, Bohua; Wu, Guanpu; Zhao, Shuai; Ma, Fangwu; Zhang, Yufei; Jian, Wu

doi:10.3390/jimaging9020052

Cited by 7 publications

(3 citation statements)

References 33 publications

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“…By incorporating the vision-, radar-, or LiDAR sensors, they can provide additional position measurement when the GNSS data is not available (inside the tunnel). For example, in the work done by Dai et al (2023), they estimated the vehicle motion by combining both LiDAR and real-time kinematic GNSS. Initially, the Normal Distributions Transform (NDT) algorithm was used to register the point clouds.…”

Section: Resultsmentioning

confidence: 99%

Vehicle Localization Based On IMU, OBD2, and GNSS Sensor Fusion Using Extended Kalman Filter

Teoh,

Em,

Aziz

2023

IJTech

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Multiple systems have been developed to identify drivers' drowsiness. Among all, the vehicle-based driver drowsiness detection system relies on lane lines to determine the lateral position of the vehicle for drowsiness detection. However, the lane lines may fade out, affecting its reliability. To resolve this issue, a vehicle localization algorithm based on the Inertial Measurement Unit (IMU), Global Navigation Satellite System (GNSS), and Onboard Diagnostics (OBD2) sensors is introduced. Initially, the kinematic bicycle model estimates the vehicle motion by using inputs from the OBD2 and IMU. Subsequently, the GNSS measurement is used to update the vehicle motion by applying the extended Kalman filter. To evaluate the algorithm's performance, the tests were conducted at the residential area in Bukit Beruang, Melaka and Multimedia University Melaka Campus. The results showed that the proposed technique achieved a total root-mean-square error of 3.892 m. The extended Kalman filter also successfully reduced the drift error by 40 -60%. Nevertheless, the extended Kalman filter suffers from the linearization error. It is recommended to employ the error-state extended Kalman filter to minimize the error. Besides, the kinematic bicycle model only generates accurate predictions at low vehicle speeds due to the assumption of zero tire slip angles. The dynamic bicycle model can be utilized to handle high-speed driving scenarios. It is also advised to integrate the LiDAR sensor since it offers supplementary position measurements, particularly in GNSS-denied environments. Lastly, the proposed technique is expected to enhance the reliability of the vehicle-based system and reduce the risk of accidents.

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Section: Resultsmentioning

confidence: 99%

Vehicle Localization Based On IMU, OBD2, and GNSS Sensor Fusion Using Extended Kalman Filter

Teoh,

Em,

Aziz

2023

IJTech

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“…Line features are commonly used to represent environments in which straight or flat structures are dominant. These features are often observed in maps constructed by means of LiDAR sensors [15], cameras [16] and RGB-D sensors [17]. In the existing literature, fusion algorithms for line features can be categorized into the following two major types: offline algorithms [18][19][20] and online algorithms [21][22][23][24].…”

Section: Literature Reviewmentioning

confidence: 99%

Reducing Redundancy in Maps without Lowering Accuracy: A Geometric Feature Fusion Approach for Simultaneous Localization and Mapping

Sun

et al. 2023

IJGI

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Geometric map features, such as line segments and planes, are receiving increasing attention due to their advantages in simultaneous localization and mapping applications. However, large structures in different environments are very likely to appear repeatedly in several consecutive time steps, resulting in redundant features in the final map. These redundant features should be properly fused, in order to avoid ambiguity and reduce the computation load. In this paper, three criteria are proposed to evaluate the closeness between any two features extracted at two different times, in terms of their included angle, feature circle overlapping and relative distance. These criteria determine whether any two features should be fused in the mapping process. Using the three criteria, all features in the global map are categorized into different clusters with distinct labels, and a fused feature is then generated for each cluster by means of least squares fitting. Two competing methods are employed for comparative verification. The comparison results indicate that using the commonly used KITTI dataset and the commercial software PreScan, the proposed feature fusion method outperforms the competing methods in terms of conciseness and accuracy.

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“…Biological studies have indicated that organisms such as mantis shrimp [7], octopuses [8], locust [9], and desert ants [10] can utilize their unique visual structures to perceive the polarization patterns of the entire sky, employing this information for self-orientation. Inspired by biomimetic navigation mechanisms, polarized navigation has demonstrated advanced performance in unmanned aerial vehicles [11,12], unmanned ground vehicles [13][14][15], and other navigation domains. With advantages such as no cumulative errors, resistance to electromagnetic interference, and robust concealment, it provides a novel solution for fully autonomous orientation in Global Navigation Satellite System (GNSS)-denied environments [16,17].…”

Section: Introductionmentioning

confidence: 99%

An Improved Bio-Orientation Method Based on Direct Sunlight Compensation for Imaging Polarization Sensor

Li,

Zhang,

Fan

et al. 2024

J. Imaging

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Direct sunlight in complex environmental conditions severely interferes with the light intensity response for imaging Polarization Sensor (PS), leading to a reduction in polarization orientation accuracy. Addressing this issue, this article analyzes the impact mechanism of direct sunlight on polarization sensor detection in a complex environment. The direct sunlight interference factor is introduced into the intensity response model of imaging polarization detection, enhancing the accuracy of the polarization detection model. Furthermore, a polarization state information analytical solution model based on direct sunlight compensation is constructed to improve the accuracy and real-time performance of the polarization state information solution. On this basis, an improved bio-orientation method based on direct sunlight compensation for imaging polarization sensor is proposed. The outdoor dynamic reorientation experiment platform is established to validate the effectiveness of the proposed method. Compared with the traditional methods, the experimental results demonstrate a 23% to 47% improvement in the polarization orientation accuracy under various solar zenith angles.

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LiDAR-Based Sensor Fusion SLAM and Localization for Autonomous Driving Vehicles in Complex Scenarios

Cited by 7 publications

References 33 publications

Vehicle Localization Based On IMU, OBD2, and GNSS Sensor Fusion Using Extended Kalman Filter

Vehicle Localization Based On IMU, OBD2, and GNSS Sensor Fusion Using Extended Kalman Filter

Reducing Redundancy in Maps without Lowering Accuracy: A Geometric Feature Fusion Approach for Simultaneous Localization and Mapping

An Improved Bio-Orientation Method Based on Direct Sunlight Compensation for Imaging Polarization Sensor

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