Localized Harris-FAST interest point detector

Karthik, O S; Varun, D.; Ramasangu, Hariharan

doi:10.1109/indicon.2016.7839107

Cited by 5 publications

(1 citation statement)

References 7 publications

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“…We used the LSD algorithm to detect line features whose threshold was set to assure that an adequate number of features can be detected. A FAST detector [ 26 ] was used to maintain a adequate number of points features in each image. The depth image and RGB image were aligned by hardware, and depth filtering was performed to reduce corresponding errors.…”

Section: Overall Structure Of the Vio Systemmentioning

confidence: 99%

Multi-Feature Nonlinear Optimization Motion Estimation Based on RGB-D and Inertial Fusion

Zhao

Miao

Zhang

2020

Sensors

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To achieve a high precision estimation of indoor robot motion, a tightly coupled RGB-D visual-inertial SLAM system is proposed herein based on multiple features. Most of the traditional visual SLAM methods only rely on points for feature matching and they often underperform in low textured scenes. Besides point features, line segments can also provide geometrical structure information of the environment. This paper utilized both points and lines in low-textured scenes to increase the robustness of RGB-D SLAM system. In addition, we implemented a fast initialization process based on the RGB-D camera to improve the real-time performance of the proposed system and designed a new backend nonlinear optimization framework. By minimizing the cost function formed by the pre-integrated IMU residuals and re-projection errors of points and lines in sliding windows, the state vector is optimized. The experiments evaluated on public datasets show that our system achieves higher accuracy and robustness on trajectories and in pose estimation compared with several state-of-the-art visual SLAM systems.

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Section: Overall Structure Of the Vio Systemmentioning

confidence: 99%