Optical measurement systems suffer from a fundamental tradeoff between the field of view (FOV), the resolution and the update rate. A compound eye has the advantages of a wide FOV, high update rate and high sensitivity to motion, providing inspiration for breaking through the constraint and realizing high-performance optical systems. However, most existing studies on artificial compound eyes are limited by complex structure and low resolution, and they focus on imaging instead of precise measurement. Here, a high-performance lensless compound eye microsystem is developed to realize target motion perception through precise and fast orientation measurement. The microsystem splices multiple sub-FOVs formed by long-focal subeyes, images targets distributed in a panoramic range into a single multiplexing image sensor, and codes the subeye aperture array for distinguishing the targets from different sub-FOVs. A wide-field and high resolution are simultaneously realized in a simple and easy-to-manufacture microelectromechanical system (MEMS) aperture array. Moreover, based on the electronic rolling shutter technique of the image sensor, a hyperframe update rate is achieved by the precise measurement of multiple time-shifted spots of one target. The microsystem achieves an orientation measurement accuracy of 0.0023° (3σ) in the x direction and 0.0028° (3σ) in the y direction in a cone FOV of 120° with an update rate ~20 times higher than the frame rate. This study provides a promising approach for achieving optical measurements with comprehensive high performance and may have great significance in various applications, such as vision-controlled directional navigation and high-dynamic target tracking, formation and obstacle avoidance of unmanned aerial vehicles.

CubeSats have been widely used in remote sensing applications such as global coverage, hotspots revisited, etc. However, due to the strict size limitation, the high-accuracy measuring instruments such as star tracker are too large to be applied in CubeSat, thus causing insufficient accuracy in satellite attitude and image positioning. In order to reduce the volume of star tracker without compromising the performance, the relationship between the volume and pointing accuracy or dynamic performance is studied and an optimization model of star tracker with a minimum volume is proposed. Compared with the traditional star tracker, a deployable star tracker with a novel deployable baffle and surrounded circuit structure is designed. The baffle consists of nested three-stage sub-baffles with a scientifically analyzed and verified taper to achieve smooth deployment and compression. The special circuit structure surrounds the lens and can be compressed in the inner sub-baffle. Therefore, the deployable star tracker can be compressed to the smallest volume and the sub-baffles can be deployed to the accurate position without self-lock risk. The experimental results verify its deployment accuracy and reliability as well as space environmental adaptability. The deployable star tracker has almost the same results on stray light suppression ability, pointing accuracy (better than 3″ $\left(3\sigma \right)$) and dynamic performance (up to 3°/s) with the traditional star tracker. Furthermore, an integrated attitude determination and control system based on the deployable star tracker for CubeSat is further designed and implemented to support high-accuracy remote sensing.

Accurate, robust and drift-free global pose estimation is a fundamental problem for autonomous vehicles. In this work, we propose a global drift-free map-based localization method for estimating the global poses of autonomous vehicles that integrates visual–inertial odometry and global localization with respect to a pre-built map. In contrast to previous work on visual–inertial localization, the global pre-built map provides global information to eliminate drift and assists in obtaining the global pose. Additionally, in order to ensure the local odometry frame and the global map frame can be aligned accurately, we augment the transformation between these two frames into the state vector and use a global pose-graph optimization for online estimation. Extensive evaluations on public datasets and real-world experiments demonstrate the effectiveness of the proposed method. The proposed method can provide accurate global pose-estimation results in different scenarios. The experimental results are compared against the mainstream map-based localization method, revealing that the proposed approach is more accurate and consistent than other methods.

All-day satellite observations are crucial for communications, navigation, and remote sensing. Current systems rely on large, long-focal-length telescopes that struggle with daytime surveillance. Compact, short-focal-length star trackers, which are designed for measuring stellar positions and determining satellite attitudes, offer flexibility and rapid tracking but typically cannot operate during the day. In this paper, we present a novel optical beacon and a star tracker for all-day ground-based satellite observations. The parameters of the star tracker and the onboard beacon system are optimized by analyzing the signal-to-noise ratio (SNR) between the atmospheric background radiation and the flux from the illumination source. In experiments with a 70 W peak illumination power and a 15° divergence angle, all-day satellite observations were achieved for a satellite at 500 km using a 15° ground-based observation angle. At night, the brightest observed satellite magnitude reached 1.19 at a distance of 570 km, while during the daytime, the SNR reached 20.17 dB at a distance of 538 km. This study significantly enhances the all-day satellite observation capability, fills the gap in daytime monitoring of satellites using star trackers, and provides strong support for future satellite space applications.

CubeSats have extensive applications in the realm of remote sensing. However, due to size constraints, attitude determination and control systems (ADCS) often adopt an integrated and densely packed structure. This leads to simple interference in the magnetic field measurements from components like reaction wheels and magnetorquers, resulting in magnetometer failures. Furthermore, the magnetic interference compromises the ability to dump the angular momentum of CubeSats. This leads to attitude control and remote sensing mission failures. To address these challenges, we introduce a dynamics-sensing, magnetic, fault-tolerant attitude control method that achieves attitude control without a priori magnetic field information generated from a magnetometer, restoring the remote sensing capabilities of CubeSats under magnetic failure. The proposed geomagnetic field sensing method, based on the temporal expansion geomagnetic vector calculate algorithm (GVCA), decouples the observation equations in three axes through control segmentation in the time domain to address the singularity issue of the Kalman coefficient matrix. Additionally, the magnetic field vector can be obtained in real time with acceptable computational consumption. Moreover, utilizing this real-time magnetic field information, the fault-tolerant control strategies and multi-mode control laws can progressively restore destabilized CubeSats to their regular states without performance degradation. We conducted numerical simulation experiments to evaluate the effectiveness of our method and system. Beyond the numerical simulations, we also built hardware systems. We designed and implemented a plug-and-play (PnP) ADCS to apply our methodology, further supporting CubeSats’ high-precision remote sensing. Furthermore, with the aid of a space environment simulation platform, we verified the performance of our system and method under conditions simulating the actual space environment. Ground testing demonstrated that our proposed method was able to identify the magnetic field and achieve high-performance attitude control with magnetic field measurement failures. The system’s pointing accuracy was better than 0.02 degrees, and the attitude stability surpassed 0.003 degrees per second.