FastORB-SLAM: a Fast ORB-SLAM Method with Coarse-to-Fine Descriptor Independent Keypoint Matching

Qiang Fu, Hongshan Yu, Xiaolong Wang, Zhengeng Yang, Hong Zhang, Ajmal Saeed Mian

Abstract:

Indirect methods for visual SLAM are gaining popularity due to their robustness to varying environments. ORB-SLAM2 [1] 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 TUM and 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.