Fast Techniques for Monocular Visual Odometry

Mirabdollah, M. Hossein; Mertsching, Bärbel

doi:10.1007/978-3-319-24947-6_24

Cited by 15 publications

(4 citation statements)

References 14 publications

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“…1) Camera height based methods: Camera height is a commonly used information for absolute scale estimation [1], [28], [29], [30], [31], [32], [33], [34], [35] based on a flat ground plane assumption. In [35] a multi-attribute cost function has been designed for selecting ground features to estimate the absolute scale.…”

Section: B Monocular-based Methodsmentioning

confidence: 99%

“…Compared with other active sensors, such as Light Detection And Ranging (Lidar), vision-based systems have several advantages: first, the camera sensor is very cheap; second, cameras can provide color, semantic and geometric information, which are important for scene understanding; finally, cameras which are passive sensors need less power consumption than the active sensors. Different from stereo or multi-cameras vision systems, monocular camera system [1], [2], [3] is a very attractive option for real-world applications due to its own merits: a single camera is easy to be mounted on the vehicle and it is also free from the burden of multi-camera calibration. Furthermore, a single fish-eye camera can also provide a relatively large field of view as stereo rigs.…”

Section: Introductionmentioning

confidence: 99%

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Ground-Plane-Based Absolute Scale Estimation for Monocular Visual Odometry

Zhou

Dai

2020

IEEE Trans. Intell. Transport. Syst.

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Recovering absolute metric scale from a monocular camera is a challenging but highly desirable problem for monocular camera-based systems. By using different kinds of cues, various approaches have been proposed for scale estimation, such as camera height, object size etc. In this paper, firstly, we summarize different kinds of scale estimation approaches. Then, we propose a robust divide and conquer absolute scale estimation method based on the ground plane and camera height by analyzing the advantages and disadvantages of different approaches. By using the estimated scale, an effective scale correction strategy has been proposed to reduce the scale drift during the Monocular Visual Odometry (VO) estimation process. Finally, the effectiveness and robustness of the proposed method have been verified on both public and self-collected image sequences.

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Section: B Monocular-based Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Ground-Plane-Based Absolute Scale Estimation for Monocular Visual Odometry

Zhou

Dai

2020

IEEE Trans. Intell. Transport. Syst.

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“…They combine multiple cues with learned models to adaptively weight per-frame observation covariances for ground plane estimation. Mirabdollah & Mertsching (2015) present a real-time and robust monocular visual odometry approach using the iterative five-point method. They obtain the location of landmarks with uncertainties using a probabilistic triangulation method and estimate the scale of the motion with low quality features on the ground plane.…”

Section: State-of-the-artmentioning

confidence: 99%

Computer Vision for Autonomous Vehicles: Problems, Datasets and State of the Art

Janai¹,

Güney²,

Behl³

et al. 2017

Preprint

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Recent years have witnessed amazing progress in AI related fields such as computer vision, machine learning and autonomous vehicles. As with any rapidly growing field, however, it becomes increasingly difficult to stay up-to-date or enter the field as a beginner. While several topic specific survey papers have been written, to date no general survey on problems, datasets and methods in computer vision for autonomous vehicles exists. This paper attempts to narrow this gap by providing a state-of-the-art survey on this topic. Our survey includes both the historically most relevant literature as well as the current state-of-the-art on several specific topics, including recognition, reconstruction, motion estimation, tracking, scene understanding and end-to-end learning. Towards this goal, we first provide a taxonomy to classify each approach and then analyze the performance of the state-of-the-art on several challenging benchmarking datasets including KITTI, ISPRS, MOT and Cityscapes. Besides, we discuss open problems and current research challenges. To ease accessibility and accommodate missing references, we will also provide an interactive platform which allows to navigate topics and methods, and provides additional information and project links for each paper.

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“…Currently, single view odometer studies are mainly based on the computer vision technique. Among them, FTMVO (Fast Techniques for Monocular Visual Odometry) technology estimates the pose using the 5-point based essential matrix developed in computer vision (Mirabdollah and Mertsching, 2015). It estimates the geometry of images by calculating essential matrix and decomposing it into the position and attitude of camera.…”

Section: Introductionmentioning

confidence: 99%

Development of a Single-View Odometer Based on Photogrammetric Bundle Adjustment

Yoon

Jung

et al. 2018

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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Recently, a vehicle is equipped with various sensors, which aim smart and autonomous functions. Single-view odometer estimates its pose using a monoscopic camera mounted on a vehicle. It was generally studied in the field of computer vision. On the other hands, photogrammetry focuses to produce precise three-dimensional position information using bundle adjustment methods. Therefore, this paper proposes to apply photogrammetric approach to single view odometer. Firstly, it performs real-time corresponding point extraction. Next, it estimates the pose using relative orientation based on coplanarity conditions. Then, scale calibration is performed to convert the estimated translation in the model space to the translation in the real space. Finally, absolute orientation is performed using more than three images. In this step, we also extract the appropriate model points through verification procedure. For experiments, we used the data provided by KITTI (Karlsruhe Institute of Technology and Toyota Technological Institute) community. This technique took 0.12 seconds of processing time per frame. The rotation estimation error was about 0.005 degree per meter and the translation estimation error was about 6.8%. The results of this study have shown the applicability of photogrammetry to visual odometry technology.

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Fast Techniques for Monocular Visual Odometry

Cited by 15 publications

References 14 publications

Ground-Plane-Based Absolute Scale Estimation for Monocular Visual Odometry

Ground-Plane-Based Absolute Scale Estimation for Monocular Visual Odometry

Computer Vision for Autonomous Vehicles: Problems, Datasets and State of the Art

Development of a Single-View Odometer Based on Photogrammetric Bundle Adjustment

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