Robust scale estimation for monocular visual odometry using structure from motion and vanishing points

Gräter, Johannes; Schwarze, Tobias; Lauer, Martin

doi:10.1109/ivs.2015.7225730

Cited by 42 publications

(36 citation statements)

References 13 publications

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“…In [42], the global scale of the 3D reconstruction is recovered by a set of pre-defined classes of objects. Other scene knowledge is also used to recover the absolute scale, such as the average pedestrian's height [38], vanishing lines [33] etc.…”

Section: B Monocular-based Methodsmentioning

confidence: 99%

“…For robust estimation, RANSAC (Random Sample Consensus) technique is used to help against outliers. In [33], two different methods are used to compute the normal vector of ground plane: 1) 3-points based RANSAC together with least-squares optimization; 2) vanishing point estimated from special scene structure is also applied for n estimation. Then the two normal vectors are fused and tracked by a Kalman Filter for the scale estimation in next frame.…”

Section: B 3d Plane Fitting Based Scale Estimationmentioning

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: B 3d Plane Fitting Based Scale Estimationmentioning

confidence: 99%

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

Zhou

Dai

2020

IEEE Trans. Intell. Transport. Syst.

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“…For example, a fixed height of the camera above the ground plane is useful to estimate scale and therefore avoid scale drift [10], [11], [12], [13]. Alternatively, the size of known objects in the environment can be used as a depth cue [14], [15].…”

Section: A Scale Drift On Monocular Slammentioning

confidence: 99%

Eliminating Scale Drift in Monocular SLAM Using Depth From Defocus

Shiozaki

Dissanayake

2018

IEEE Robot. Autom. Lett.

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Abstract-This paper presents a novel approach to correct errors caused by accumulated scale drift in monocular SLAM. It is shown that the metric scale can be estimated using information gathered through monocular SLAM and image blur due to defocus. A nonlinear least squares optimization problem is formulated to integrate depth estimates from defocus to monocular SLAM. An algorithm to process the output keyframe and feature location estimates generated by a monocular SLAM algorithm to correct for scale drift at selected local regions of the environment is presented. The proposed algorithm is experimentally evaluated by processing the output of ORB-SLAM [1] to obtain accurate metric scale maps from a monocular camera without any prior knowledge about the scene.

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“…The input is a temporal image sequence, output are the camera poses and a sparse reconstruction of the environment. Scale information can be retained from a second, calibrated camera with known baseline [6], structure inherent information, such as the distance to the ground plane [19], IMU [20] or in our case depth measurements from a LIDAR. Scale can optionally be used for estimating the frame to frame motion as well.…”

Section: Introduction and Related Workmentioning

confidence: 99%

LIMO: Lidar-Monocular Visual Odometry

Graeter

Wilczynski

Lauer

2018

2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

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205

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Higher level functionality in autonomous driving depends strongly on a precise motion estimate of the vehicle. Powerful algorithms have been developed. However, their great majority focuses on either binocular imagery or pure LIDAR measurements. The promising combination of camera and LI-DAR for visual localization has mostly been unattended. In this work we fill this gap, by proposing a depth extraction algorithm from LIDAR measurements for camera feature tracks and estimating motion by robustified keyframe based Bundle Adjustment. Semantic labeling is used for outlier rejection and weighting of vegetation landmarks. The capability of this sensor combination is demonstrated on the competitive KITTI dataset, achieving a placement among the top 15. The code is released to the community.

show abstract

Robust scale estimation for monocular visual odometry using structure from motion and vanishing points

Cited by 42 publications

References 13 publications

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

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

Eliminating Scale Drift in Monocular SLAM Using Depth From Defocus

LIMO: Lidar-Monocular Visual Odometry

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