1 research outputs found
Direct Visual-Inertial Odometry with Semi-Dense Mapping
The paper presents a direct visual-inertial odometry system. In particular, a
tightly coupled nonlinear optimization based method is proposed by integrating
the recent advances in direct dense tracking and Inertial Measurement Unit
(IMU) pre-integration, and a factor graph optimization is adapted to estimate
the pose of the camera and rebuild a semi-dense map. Two sliding windows are
maintained in the proposed approach. The first one, based on Direct Sparse
Odometry (DSO), is to estimate the depths of candidate points for mapping and
dense visual tracking. In the second one, measurements from the IMU
pre-integration and dense visual tracking are fused probabilistically using a
tightly-coupled, optimization-based sensor fusion framework. As a result, the
IMU pre-integration provides additional constraints to suppress the scale drift
induced by the visual odometry. Evaluations on real-world benchmark datasets
show that the proposed method achieves competitive results in indoor scenes