Efficient Globally-Optimal Correspondence-Less Visual Odometry for Planar Ground Vehicles

Gao, Ling; Su, Junyan; Cui, Jiadi; Zeng, Xiangchen; Peng, Xin; Kneip, Laurent

doi:10.1109/icra40945.2020.9196595

Cited by 12 publications

(14 citation statements)

References 27 publications

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“…Exploiting the BnB technique, our proposed method under similar planar constraints offers better robustness in outlier rejection. Similarly, using the restrictive Ackermann steering model, Gao et al (2020) propose a globallyoptimal solution under planar motion by the BnB technique which is the most relevant study to ours. In the study of Gao et al (2020), the camera faces downward to fix depth so that homography can also be applied.…”

Section: Related Workmentioning

confidence: 99%

“…Similarly, using the restrictive Ackermann steering model, Gao et al (2020) propose a globallyoptimal solution under planar motion by the BnB technique which is the most relevant study to ours. In the study of Gao et al (2020), the camera faces downward to fix depth so that homography can also be applied. Through parameterizing planar rotation and translation into trigonometric functions, the researcher computes the bound in each branch efficiently.…”

Section: Related Workmentioning

confidence: 99%

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Globally-Optimal Inlier Maximization for Relative Pose Estimation Under Planar Motion

et al. 2022

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Planar motion constraint occurs in visual odometry (VO) and SLAM for Automated Guided Vehicles (AGVs) or mobile robots in general. Conventionally, two-point solvers can be nested to RANdom SAmple Consensus to reject outliers in real data, but the performance descends when the ratio of outliers goes high. This study proposes a globally-optimal Branch-and-Bound (BnB) solver for relative pose estimation under general planar motion, which aims to figure out the globally-optimal solution even under a quite noisy environment. Through reasonable modification of the motion equation, we decouple the relative pose into relative rotation and translation so that a simplified bounding strategy can be applied. It enhances the efficiency of the BnB technique. Experimental results support the global optimality and demonstrate that the proposed method performs more robustly than existing approaches. In addition, the proposed algorithm outperforms state-of-art methods in global optimality under the varying level of outliers.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Globally-Optimal Inlier Maximization for Relative Pose Estimation Under Planar Motion

et al. 2022

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“…Other works constrain the problem to car-like vehicles [11][12][13]. By assuming the Ackermann steering model, the general planar motion can be restricted to 2DoF.…”

Section: Related Workmentioning

confidence: 99%

“…By assuming the Ackermann steering model, the general planar motion can be restricted to 2DoF. Gao et al [11] propose a keypoint-less method to recover the ego motion of car-like steering models using an optimal image registration technique based on global branch-and-bound optimization. Peng et al [12] use an event-based camera system to overcome the downsides of a frame-based camera such as latency, motion blur and low dynamic range.…”

Section: Related Workmentioning

confidence: 99%

Unsupervised deep learning based ego motion estimation with a downward facing camera

Gilles

Ibrahimpasic²

2021

Vis Comput

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Knowing the robot's pose is a crucial prerequisite for mobile robot tasks such as collision avoidance or autonomous navigation. Using powerful predictive models to estimate transformations for visual odometry via downward facing cameras is an understudied area of research. This work proposes a novel approach based on deep learning for estimating ego motion with a downward looking camera. The network can be trained completely unsupervised and is not restricted to a specific motion model. We propose two neural network architectures based on the Early Fusion and Slow Fusion design principle: “EarlyBird” and “SlowBird”. Both networks share a Spatial Transformer layer for image warping and are trained with a modified structural similarity index (SSIM) loss function. Experiments carried out in simulation and for a real world differential drive robot show similar and partially better results of our proposed deep learning based approaches compared to a state-of-the-art method based on fast Fourier transformation.

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“…Camera motion estimation is one of the most important technologies in many applications, such as automatic driving and assistive technologies. In particular, motion estimation by pointing a camera at a road surface has an advantage in that objects that can affect it, such as automobiles and pedestrians, are usually not easily visible [1][2][3][4][5][6]. Teshima et al [1] proposed a method to estimate the position and orientation of a vehicle from sequential images of the ground using the known homography obtained from a normal camera.…”

Section: Introduction 1backgroundsmentioning

confidence: 99%

Accuracy and Speed Improvement of Event Camera Motion Estimation Using a Bird’s-Eye View Transformation

Ozawa

Sekikawa

Saitō

2022

Sensors

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Event cameras are bio-inspired sensors that have a high dynamic range and temporal resolution. This property enables motion estimation from textures with repeating patterns, which is difficult to achieve with RGB cameras. Therefore, motion estimation of an event camera is expected to be applied to vehicle position estimation. An existing method, called contrast maximization, is one of the methods that can be used for event camera motion estimation by capturing road surfaces. However, contrast maximization tends to fall into a local solution when estimating three-dimensional motion, which makes correct estimation difficult. To solve this problem, we propose a method for motion estimation by optimizing contrast in the bird’s-eye view space. Instead of performing three-dimensional motion estimation, we reduced the dimensionality to two-dimensional motion estimation by transforming the event data to a bird’s-eye view using homography calculated from the event camera position. This transformation mitigates the problem of the loss function becoming non-convex, which occurs in conventional methods. As a quantitative experiment, we created event data by using a car simulator and evaluated our motion estimation method, showing an improvement in accuracy and speed. In addition, we conducted estimation from real event data and evaluated the results qualitatively, showing an improvement in accuracy.

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Efficient Globally-Optimal Correspondence-Less Visual Odometry for Planar Ground Vehicles

Cited by 12 publications

References 27 publications

Globally-Optimal Inlier Maximization for Relative Pose Estimation Under Planar Motion

Globally-Optimal Inlier Maximization for Relative Pose Estimation Under Planar Motion

Unsupervised deep learning based ego motion estimation with a downward facing camera

Accuracy and Speed Improvement of Event Camera Motion Estimation Using a Bird’s-Eye View Transformation

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