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Consider a crane whose telescopic boom rotates with an angular velocity of 0.04 rad/s and angular acceleration of 0.02 rad/s2. Along with the rotation, the boom also extends linearly with a uniform speed of 5 m/s. The extension of the boom is measured at point D, which is measured with respect to the fixed point C on the other end of the boom. For the given instant, the distance between points C and D is 60 meters.
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Related Experiment Video

Updated: Sep 29, 2025

In Vivo Quantification of Hip Arthrokinematics during Dynamic Weight-bearing Activities using Dual Fluoroscopy
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Globally-Optimal Inlier Maximization for Relative Pose Estimation Under Planar Motion.

Haotian Liu1,2, Guang Chen2,3, Yinlong Liu3

  • 1State Key Laboratory of Vehicle NVH and Safety Technology, Chongqing, China.

Frontiers in Neurorobotics
|March 21, 2022
PubMed
Summary

This study introduces a globally-optimal Branch-and-Bound solver for visual odometry (VO) and SLAM, improving relative pose estimation for mobile robots even with high outlier ratios. The new method enhances robustness and efficiency in noisy environments.

Keywords:
Automated Guided Vehicle (AGV)Branch-and-Bound (BnB)inlier set maximizationrelative pose estimationrotation and translation estimation

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Area of Science:

  • Robotics
  • Computer Vision
  • Artificial Intelligence

Background:

  • Planar motion constraints are common in visual odometry (VO) and simultaneous localization and mapping (SLAM) for Automated Guided Vehicles (AGVs) and mobile robots.
  • Current methods using two-point solvers with RANdom SAmple Consensus (RANSAC) struggle with high outlier ratios, degrading performance.

Purpose of the Study:

  • To propose a globally-optimal Branch-and-Bound (BnB) solver for robust relative pose estimation under general planar motion.
  • To enhance the efficiency and robustness of BnB for VO and SLAM in noisy conditions.

Main Methods:

  • Developed a novel Branch-and-Bound (BnB) solver for relative pose estimation.
  • Modified the motion equation to decouple relative rotation and translation, enabling a simplified bounding strategy.
  • Applied the BnB technique to address challenges in visual odometry and SLAM with planar motion constraints.

Main Results:

  • The proposed BnB solver achieves global optimality even in highly noisy environments.
  • The method demonstrates superior robustness compared to existing approaches, especially under varying outlier levels.
  • Experimental results validate the global optimality and enhanced performance of the new algorithm.

Conclusions:

  • The developed Branch-and-Bound solver offers a robust and efficient solution for relative pose estimation in VO and SLAM under planar motion.
  • This approach significantly improves performance in environments with a high proportion of outliers.
  • The study advances the state-of-the-art in mobile robot localization and mapping.