Visual acuity, defocus curve and patient satisfaction of a new hybrid EDOF-multifocal diffractive intraocular lens

Purpose: To evaluate the visual acuity, defocus curve and visual quality of life following bilateral implantation of a new hybrid extended depth of focus (EDOF)-multifocal diffractive intraocular lens (IOL). Methods: Patients who underwent cataract surgery with bilateral implantation of Tecnis Synergy IOL were enrolled in this study. The main outcomes were defined as uncorrected distant, intermediate and near visual acuities (UDVA, UIVA and UNVA, respectively), defocus curve and VF-14 questionnaire aimed for visual performance. Results: The mean ± SD values for binocular UDVA, UIVA and UNVA were −0.01 ± 0.04, 0.05 ± 0.03 and 0.03 ± 0.05 logMAR, respectively. Defocus curve showed a continuous vision above 0.16 logMAR between +0.50 D and −2.50 D. Patients had good visual performance with mean VF-14 scores above 3 points. Driving at night (3.47 ± 0.64) was found to be the most difficult task to perform. Spectacle independence was 100% for both distant and near vision. Conclusion: Bilateral implantation of Tecnis Synergy IOL in cataract patients shows good visual acuity and performance, covering a good visual range without any need of spectacles.

Fast Attitude Estimation System for Unmanned Ground Vehicle Based on Vision/Inertial Fusion

The attitude estimation system based on vision/inertial fusion is of vital importance and great urgency for unmanned ground vehicles (UGVs) in GNSS-challenged/denied environments. This paper aims to develop a fast vision/inertial fusion system to estimate attitude; which can provide attitude estimation for UGVs during long endurance. The core idea in this paper is to integrate the attitude estimated by continuous vision with the inertial pre-integration results based on optimization. Considering that the time-consuming nature of the classical methods comes from the optimization and maintenance of 3D feature points in the back-end optimization thread, the continuous vision section calculates the attitude by image matching without reconstructing the environment. To tackle the cumulative error of the continuous vision and inertial pre-integration, the prior attitude information is introduced for correction, which is measured and labeled by an off-line fusion of multi-sensors. Experiments with the open-source datasets and in road environments have been carried out, and the results show that the average attitude errors are 1.11° and 1.96°, respectively. The road test results demonstrate that the processing time per frame is 24 ms, which shows that the proposed system improves the computational efficiency.