2-Entity RANSAC for robust visual localization in changing environment

Jiao, Yanmei; Wang, Yue; Fu, Bo; Ding, Xiaqing; Tan, Qimeng; Chen, Lei; Xiong, Rong

doi:10.1109/iros40897.2019.8967671

Cited by 12 publications

(12 citation statements)

References 35 publications

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“…• The source code of proposed monocular and multiple camera algorithms are available on github 2 which is a contribution to the community for comparative study. This paper completes our previous work [23] by generalizing the minimal solutions from mono-camera system to multicamera system. We also extend the strategy selection mechanism to an end-to-end learning-based method and present a new feature scoring network to perform a weighted 2entity RANSAC.…”

supporting

confidence: 64%

2-Entity Random Sample Consensus for Robust Visual Localization: Framework, Methods, and Verifications

Jiao

Wang

Ding

et al. 2021

IEEE Trans. Ind. Electron.

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Robust and efficient visual localization is essential for numerous robotic applications. However, it remains a challenging problem especially when significant environmental or perspective changes present, as there are high percentage of outliers, i.e. incorrect feature matches, between the query image and the map. In this paper, we propose a novel 2-entity RANSAC framework using 3D-2D point and line feature matches for visual localization with the aid of inertial measurements and derive minimal closed form solutions using only 1 point 1 line or 2 point matches for both monocular and multi-camera system. The proposed 2-entity RANSAC can achieve higher robustness against outliers as multiple types of features are utilized and the number of matches needed to compute a pose is reduced. Furthermore, we propose a learning-based sampling strategy selection mechanism and a feature scoring network to be adaptive to different environmental characteristics such as structured and unstructured. Finally, both simulation and real-world experiments are performed to validate the robustness and effectiveness of the proposed method in scenarios with long-term and perspective changes 1 . Index Terms-Camera pose estimation, random sample consensus (RANSAC), robust localization. I. INTRODUCTIONL OCALIZATION is a fundamental capability for mobile robots with applications to driverless cars, unmanned aerial vehicles and so on [1] [2]. Visual localization attracts more attention as the cameras are low-cost, lightweight and versatile compared with Light Detection and Ranging (Li-DAR). The general idea of visual localization is to recover the translation and rotation of the query camera based on feature matches using various descriptors (e.g. FAST [3], SIFT

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supporting

confidence: 64%

2-Entity Random Sample Consensus for Robust Visual Localization: Framework, Methods, and Verifications

Jiao

Wang

Ding

et al. 2021

IEEE Trans. Ind. Electron.

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“…Note that (22) only considers one landmark. We need to stack (22) for all matched landmarks to get the final observation function:…”

Section: B Consistent Global Positioningmentioning

confidence: 99%

“…For real world data sets, the matching procedure is conducted as follows: We first utilize R2D2 [21] to extract new features on the current query frame and match with features in map keyframes. When there are enough matches, 3D-2D pairs (3D from map landmarks and 2D from current frame features) are fed into the robust pose solver in [22], after which the accurate and robust 3D-2D pairs are obtained.…”

Section: B Experiments On Real World Data Setsmentioning

confidence: 99%

Toward Consistent Drift-free Visual Inertial Localization on Keyframe Based Map

Zhang,

Jiao,

Huang

et al. 2021

Preprint

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Global localization is essential for robots to perform further tasks like navigation. In this paper, we propose a new framework to perform global localization based on a filterbased visual-inertial odometry framework MSCKF. To reduce the computation and memory consumption, we only maintain the keyframe poses of the map and employ Schmidt-EKF to update the state. This global localization framework is shown to be able to maintain the consistency of the state estimator. Furthermore, we introduce a re-linearization mechanism during the updating phase. This mechanism could ease the linearization error of observation function to make the state estimation more precise. The experiments show that this mechanism is crucial for large and challenging scenes. Simulations and experiments demonstrate the effectiveness and consistency of our global localization framework.

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“…In [25] [26] [27], RANSAC is extended to line features. When inertial measurements are provided, the DoF of the problem is reduced, which is utilized by RANSAC to improve the robustness in [28] [29], and extended to both point and line correspondences in [30]. As RANSAC is developed on randomized sampling theory, it is simple to implement and has good performance on scenarios with moderate outliers.…”

Section: B Random Sample Consensusmentioning

confidence: 99%

“…According to (30) and ( 33), we have linear constraints of the translation t. With proper variable substitutions among the constraints, and the globally observable pitch and roll angles from inertial measurements, we can eliminate t, reduce SO(3) to [−π, π], and derive TIM as…”

Section: Decoupling Translation and Rotationmentioning

confidence: 99%

Globally optimal consensus maximization for robust visual inertial localization in point and line map

Jiao¹,

Wang²,

Fu³

et al. 2020

Preprint

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Map based visual inertial localization is a crucial step to reduce the drift in state estimation of mobile robots. The underlying problem for localization is to estimate the pose from a set of 3D-2D feature correspondences, of which the main challenge is the presence of outliers, especially in changing environment. In this paper, we propose a robust solution based on efficient global optimization of the consensus maximization problem, which is insensitive to high percentage of outliers. We first introduce translation invariant measurements (TIMs) for both points and lines to decouple the consensus maximization problem into rotation and translation subproblems, allowing for a two-stage solver with reduced search space. Then we show that (i) the rotation can be estimated by minimizing TIMs using only 1-dimensional branch-and-bound (BnB), (ii) the translation can be estimated by running 1-dimensional search for each of the three axes with prioritized progressive voting. Compared with the popular randomized solver, our solver achieves deterministic global convergence without requiring an initial value. Furthermore, ours is exponentially faster compared with existing BnB based methods. Finally, our experiments on both simulation and real-world datasets demonstrate that the proposed method gives accurate pose estimation even in the presence of 90% outliers (only 2 inliers).

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2-Entity RANSAC for robust visual localization in changing environment

Cited by 12 publications

References 35 publications

2-Entity Random Sample Consensus for Robust Visual Localization: Framework, Methods, and Verifications

2-Entity Random Sample Consensus for Robust Visual Localization: Framework, Methods, and Verifications

Toward Consistent Drift-free Visual Inertial Localization on Keyframe Based Map

Globally optimal consensus maximization for robust visual inertial localization in point and line map

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