Lane Detection Aided Online Dead Reckoning for GNSS Denied Environments

Jeon, Jin-Hwan; Hwang, Y. S.; Jeong, Yongseop; Park, Sangdon; Kweon, In So; Choi, Seibum B.

doi:10.3390/s21206805

Cited by 7 publications

(5 citation statements)

References 38 publications

(37 reference statements)

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“…To further improve the dead reckoning localisation based on inertia navigation system (INS), Jeon et al (2021)…”

Section: Localisationmentioning

confidence: 99%

Autonomous Harvesting Robot for Oil Palm Plantation: A Review

HAO

2024

JOPR

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Due to the continuous labour crisis, the oil palm industry in Malaysia has lost an estimated worth of RM10.46 billion unharvested fruit in the first five months of the year 2022. A robotic system in automating the harvesting process of the oil palm fresh fruit bunches (FFB) is proposed to solve the existing problems related to oil palm harvesting and further enhances the development of oil palm harvesting technologies. This article aims to review the six identified key technologies for solving the technological challenges in the development of this robotic system. The key technologies are as follows: (1) oil palm ripeness detection;(2) oil palm cutting mechanism; (3) tree climbing mechanism; (4) motion trajectory planning for fruit harvesting manipulator; (5) localisation; (6) navigation and obstacle avoidance. Six criteria for successful implementation of the proposed harvesting robot are discussed followed by recommendations on the type of technology used. The integration of these technologies as a complete robotic system is analysed. Prediction on the trend of technological development in oil palm harvesting is discussed.

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“…To further improve the dead reckoning localisation based on inertia navigation system (INS), Jeon et al (2021)…”

Section: Localisationmentioning

confidence: 99%

Autonomous Harvesting Robot for Oil Palm Plantation: A Review

HAO

2024

JOPR

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“…The perception methods such as object classification, object tracking, and vehicle localisations make sense of raw data that are brought after the pre-processing step by sensors and V2X communication. AD and ICC can use cameras to estimate velocity using visual odometry, yaw, roll, and pitch rates, detect road lines [ 149 , 150 ], road curve angle and road bank angle, intersections, railroad, and pedestrian crossings, road surface type and conditions [ 118 ], road lanes and boundaries [ 151 , 152 ], road signs including warnings and speed limits [ 153 ], and horizontal marking detection and recognition [ 154 ], road damage, potholes, and distress detection [ 155 ], location [ 156 ], velocity and displacement of the vehicle [ 157 ] and other vehicles on the road even using their taillights [ 158 ]. ICC systems can use previously described advanced sensors to acquire horizon prediction, and longitudinal and lateral road surface slopes, which, together with in-vehicle IMU and GNSS/INS, will enable a more accurate decomposition of linear and gravitation-caused acceleration.…”

Section: Common Controller Layout For Automated Vehiclesmentioning

confidence: 99%

Review of Integrated Chassis Control Techniques for Automated Ground Vehicles

Skrickij,

Kojis,

Šabanovič

et al. 2024

Sensors

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Integrated chassis control systems represent a significant advancement in the dynamics of ground vehicles, aimed at enhancing overall performance, comfort, handling, and stability. As vehicles transition from internal combustion to electric platforms, integrated chassis control systems have evolved to meet the demands of electrification and automation. This paper analyses the overall control structure of automated vehicles with integrated chassis control systems. Integration of longitudinal, lateral, and vertical systems presents complexities due to the overlapping control regions of various subsystems. The presented methodology includes a comprehensive examination of state-of-the-art technologies, focusing on algorithms to manage control actions and prevent interference between subsystems. The results underscore the importance of control allocation to exploit the additional degrees of freedom offered by over-actuated systems. This paper systematically overviews the various control methods applied in integrated chassis control and path tracking. This includes a detailed examination of perception and decision-making, parameter estimation techniques, reference generation strategies, and the hierarchy of controllers, encompassing high-level, middle-level, and low-level control components. By offering this systematic overview, this paper aims to facilitate a deeper understanding of the diverse control methods employed in automated driving with integrated chassis control, providing insights into their applications, strengths, and limitations.

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“…EKF can, to some extent, handle system nonlinearity up to the point that the noise statistic such as covariance matrixes of the process noise is accurate, otherwise the estimation can either be inaccurate or diverge [5]. Therefore, it can be inferred that EKF error estimates tend to undervalue state uncertainties [6]. UKF, to a great extent, compensates the deficiencies of the EKF in system nonlinearity, but it still suffers from the fact that rounding error and approximation error become larger and larger when nonlinearity of the system increases [7].…”

Section: Literature Reviewmentioning

confidence: 99%

Landing System Development Based on Inverse Homography Range Camera Fusion (IHRCF)

Sefidgar

Landry

2022

Sensors

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The Unmanned Aerial Vehicle (UAV) is one of the most remarkable inventions of the last 100 years. Much research has been invested in the development of this flying robot. The landing system is one of the more challenging aspects of this system’s development. Artificial Intelligence (AI) has become the preferred technique for landing system development, including reinforcement learning. However, current research is more focused is on system development based on image processing and advanced geometry. A novel calibration based on our previous research had been used to ameliorate the accuracy of the AprilTag pose estimation. With the help of advanced geometry from camera and range sensor data, a process known as Inverse Homography Range Camera Fusion (IHRCF), a pose estimation that outperforms our previous work, is now possible. The range sensor used here is a Time of Flight (ToF) sensor, but the algorithm can be used with any range sensor. First, images are captured by the image acquisition device, a monocular camera. Next, the corners of the landing landmark are detected through AprilTag detection algorithms (ATDA). The pixel correspondence between the image and the range sensor is then calculated via the calibration data. In the succeeding phase, the planar homography between the real-world locations of sensor data and their obtained pixel coordinates is calculated. In the next phase, the pixel coordinates of the AprilTag-detected four corners are transformed by inverse planar homography from pixel coordinates to world coordinates in the camera frame. Finally, knowing the world frame corner points of the AprilTag, rigid body transformation can be used to create the pose data. A CoppeliaSim simulation environment was used to evaluate the IHRCF algorithm, and the test was implemented in real-time Software-in-the-Loop (SIL). The IHRCF algorithm outperformed the AprilTag-only detection approach significantly in both translational and rotational terms. To conclude, the conventional landmark detection algorithm can be ameliorated by incorporating sensor fusion for cameras with lower radial distortion.

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Lane Detection Aided Online Dead Reckoning for GNSS Denied Environments

Cited by 7 publications

References 38 publications

Autonomous Harvesting Robot for Oil Palm Plantation: A Review

Autonomous Harvesting Robot for Oil Palm Plantation: A Review

Review of Integrated Chassis Control Techniques for Automated Ground Vehicles

Landing System Development Based on Inverse Homography Range Camera Fusion (IHRCF)

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