2.5D vehicle odometry estimation

Eising, Ciaran; Pereira, Leroy-Francisco; Horgan, Jonathan; Selvaraju, Anbuchezhiyan; McDonald, John; Moran, P. R.

doi:10.1049/itr2.12143

Cited by 5 publications

(3 citation statements)

References 42 publications

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“…There was no explicit motion compensation, but it was mentioned as future work. Mariotti et al [79] uses a classical approach to accomplishing this task based on vehicle odometry [127]. Spherical coordinate transformation of optical flow was performed and the positive height, depth, and epipolar constraints were adapted to work in this setup.…”

Section: B Geometric Tasks 1) Depth Estimationmentioning

confidence: 99%

Surround-View Fisheye Camera Perception for Automated Driving: Overview, Survey & Challenges

Kumar

Eising

Witt

et al. 2023

IEEE Trans. Intell. Transport. Syst.

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Surround-view fisheye cameras are commonly used for near-field sensing in automated driving. Four fisheye cameras on four sides of the vehicle are sufficient to cover 360 • around the vehicle capturing the entire near-field region. Some primary use cases are automated parking, traffic jam assist, and urban driving. There are limited datasets and very little work on near-field perception tasks as the focus in automotive perception is on far-field perception. In contrast to far-field, surround-view perception poses additional challenges due to high precision object detection requirements of 10cm and partial visibility of objects. Due to the large radial distortion of fisheye cameras, standard algorithms cannot be extended easily to the surround-view use case. Thus, we are motivated to provide a self-contained reference for automotive fisheye camera perception for researchers and practitioners. Firstly, we provide a unified and taxonomic treatment of commonly used fisheye camera models. Secondly, we discuss various perception tasks and existing literature. Finally, we discuss the challenges and future direction.

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Section: B Geometric Tasks 1) Depth Estimationmentioning

confidence: 99%

Surround-View Fisheye Camera Perception for Automated Driving: Overview, Survey & Challenges

Kumar

Eising

Witt

et al. 2023

IEEE Trans. Intell. Transport. Syst.

View full text Add to dashboard Cite

show abstract

“…There was no explicit motion compensation but it was mentioned as future work. Mariotti et al [74] uses a classical approach to accomplishing this task based on vehicle odometry [123]. Spherical coordinate transformation of optical flow was performed and the positive height, depth, and epipolar constraints were adapted to work in this setup.…”

Section: B Geometric Tasksmentioning

confidence: 99%

Surround-view Fisheye Camera Perception for Automated Driving: Overview, Survey and Challenges

Kumar¹,

Eising²,

Witt³

et al. 2022

Preprint

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Surround-view fisheye cameras are commonly used for near-field sensing in automated driving. Four fisheye cameras on four sides of the vehicle are sufficient to cover 360°around the vehicle capturing the entire near-field region. Some primary use cases are automated parking, traffic jam assist, and urban driving. There are limited datasets and very little work on near-field perception tasks as the main focus in automotive perception is on far-field perception. In contrast to far-field, surround-view perception poses additional challenges due to high precision object detection requirements of 10cm and partial visibility of objects. Due to the large radial distortion of fisheye cameras, standard algorithms can not be extended easily to the surround-view use case. Thus we are motivated to provide a selfcontained reference for automotive fisheye camera perception for researchers and practitioners. Firstly, we provide a unified and taxonomic treatment of commonly used fisheye camera models. Secondly, we discuss various perception tasks and existing literature. Finally, we discuss the challenges and future direction. I. INTRODUCTIONSurround-view systems use four sensors to form a network with overlap regions, sufficient to cover the near-field area around the car. Figure 1 shows the four views of a typical surround-view system, along with a representation of the typical parking use-case. Wide-angle views exceeding 180 • are used for this near-field sensing. Any perception algorithm must consider the significant fisheye distortion inherent with such camera systems. This is a significant challenge, as most work in computer vision focuses on narrow field-of-view cameras with mild radial distortion. However, as such camera systems are more widely deployed, work has been completed in this area. It is the aim of this paper to give the reader an overview of surround view cameras (e.g., image formation, configuration and rectification), to survey the existing state of the art, and to provide insights into the current challenges in the area.In theory, the field-of-view of a pinhole camera is 180 • . However, in practice, due to the practical limitations of the size of the aperture and imager, it is not easy to get over 80 • , as illustrated in Figure 2 (top). Fisheye lenses are commonly used to effectively increase the field-of-view to 180 • or more. It is interesting to note that the term fisheye is a bit of a misnomer, as illustrated in Figure 2 (bottom). Due to the bending of light rays due to refraction at the junction of water and air surface, a large field-of-view of nearly 180 • is compressed to a smaller V. Ravi Kumar, C. Witt, and S. Yogamani are with Valeo. C. Eising is with the Dept. of Electronic and Computer Engineering at the University of Limerick. V. Ravi Kumar and C. Eising are co-first authors.

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“…In addition to vehicle dynamics control, different vehicle models with different complexity are also adopted for other specific research targets, such as vehicle pose estimation [25,26] and vehicle sideslip angle estimation [27]. Although model simplification is beneficial for controller design, it potentially makes the vehicle model deviate from the real vehicle dynamics when large and rapid control inputs are required for vehicle dynamic control during emergency conditions such as collision avoidance [28].…”

Section: Introductionmentioning

confidence: 99%

Impact of the tyre dynamics on autonomous vehicle path following control with front wheel steering and differential motor torque

Zhou

Yang

Walker

et al. 2023

IET Intelligent Trans Sys

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This paper investigates the impact of tyre dynamics on autonomous vehicle dynamic control. The commonly used 2 degrees of freedom bicycle model and a detailed vehicle model considering tyre dynamics are built, respectively, to describe the vehicle dynamics. The two models are firstly compared with the response to front wheel steering input and differential motor torque input. The transient vehicle body vibrations are clearly demonstrated by the detailed model but overlooked by the bicycle model. The influence of tyre nonlinearity on path offset is also revealed. Then, the path following control strategy is designed based on the linear quadratic regulator control to compare the two models from the path following performance perspective. The double lane change simulation is conducted. The results show that at low vehicle speed, the path following errors of two models are very close, which proves the rationality of using the bicycle model to represent the vehicle dynamics in mild and moderate driving conditions. However, when the path following is conducted with high vehicle speed, the required lateral tyre force is large, so the tyres work with large slip angles and the tyre nonlinearity generates significant deviation on the path following errors between the two models.

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2.5D vehicle odometry estimation

Cited by 5 publications

References 42 publications

Surround-View Fisheye Camera Perception for Automated Driving: Overview, Survey & Challenges

Surround-View Fisheye Camera Perception for Automated Driving: Overview, Survey & Challenges

Surround-view Fisheye Camera Perception for Automated Driving: Overview, Survey and Challenges

Impact of the tyre dynamics on autonomous vehicle path following control with front wheel steering and differential motor torque

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