Indirect methods for visual SLAM are gaining popularity due to their robustness to varying environments . ORB-SLAM2  is a benchmark method in this domain, however, the computation of descriptors in ORB-SLAM2 is time-consuming and the descriptors cannot be reused unless a frame is selected as a keyframe.To overcome these problems, we present FastORB-SLAM which is light-weight and efficient as it tracks keypoints between adjacent frames without computing descriptors. To achieve this, a two-stage coarse-to-fine descriptor independent keypoint matching method is proposed based on sparse optical flow. In the first stage, we first predict initial keypoint correspondences via a uniform acceleration motion model and then robustly establish the correspondences via a pyramid-based sparse optical flow tracking method . In the second stage, we leverage  motion smoothness and the epipolar constraint to refine the correspondences. In particular, our method computes descriptors only for keyframes. We test FastORB-SLAM with an RGBD camera on \textit{TUM} and \textit{ICL-NUIM}  datasets and compare its accuracy and efficiency to nine existing RGBD SLAM methods. Qualitative and quantitative results show that our method achieves state-of-the-art performance in accuracy and is about twice as fast as the ORB-SLAM2 
Indirect methods for visual SLAM are gaining popularity due to their robustness to environmental variations . ORB-SLAM2 \mbox{%DIFAUXCMD orbslam2 is a benchmark method in this domain, however, it consumes significant time for computing descriptors that never get reused unless a frame is selected as a keyframe.To overcome these problems, we present FastORB-SLAM which is lightweight and efficient as it tracks keypoints between adjacent frames without computing descriptors. To achieve this, a two stage coarse-to-fine descriptor independent keypoint matching method is proposed based on sparse optical flow. In the first stage, we predict initial keypoint correspondences via a simple but effective motion model and then robustly establish the correspondences via pyramid-based sparse optical flow tracking . In the second stage, we leverage the constraints of the motion smoothness and epipolar geometry to refine the correspondences. In particular, our method computes descriptors only for keyframes. We test FastORB-SLAM on \textit{TUM} and \textit{ICL-NUIM} RGB-D datasets and compare its accuracy and efficiency to nine existing RGB-D SLAM methods. Qualitative and quantitative results show that our method achieves state-of-the-art accuracy and is about twice as fast as the ORB-SLAM2 .
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varying environments
environmental variations
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\mbox{%DIFAUXCMD orbslam2
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the computation of descriptors in ORB-SLAM2 is time-consuming and the descriptors cannot be
it consumes significant time for computing descriptors that never get
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light-weight
lightweight
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two-stage
two stage
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first
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uniform acceleration
simple but effective
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a
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method
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the constraints of the
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the epipolar constraint
epipolar geometry
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with an RGBD camera
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RGB-D
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RGBD
RGB-D
D
performance in
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.