说明:In this paper, we present a novel method to fuse observations from an inertial measurement unit (IMU) and visual
sensors, such that initial conditions of the inertial integration,
including gravity estimation, can be recovered quickly and in a
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说明:Abstract: Current approaches for visual-inertial odometry
(VIO) are able to attain highly accurate state estimation via
nonlinear optimization. However, real-time optimization quickly
becomes infeasible as the trajectory grows over time; this prob <lianghu3124> 上传 | 大小:3mb
说明:Abstract— In recent years there have been excellent results
in Visual-Inertial Odometry techniques, which aim to compute
the incremental motion of the sensor with high accuracy
and robustness. However these approaches lack the capability
to close <lianghu3124> 上传 | 大小:663kb