The integrated inertial system, typically integrating an IMU and an exteroceptive sensor, such as radar, light detection and ranging (LiDAR), and camera, has been widely accepted and applied in modern robotic applications for ego-motion estimation, motion control, or autonomous exploration. To improve system accuracy, robustness, and further usability, both multiple and various sensors are generally resiliently integrated, which benefits the system performance regarding failure tolerance, perception capability, and environment compatibility. For such systems, accurate and consistent spatiotemporal calibration is required to maintain a unique spatiotemporal framework for multisensor fusion. Considering that most existing calibration methods first, are generally oriented to specific integrated inertial systems, second, often focus on spatial-only determination, and third, usually require artificial targets, lacking convenience and usability, we proposeiKalibr:a unified targetless spatiotemporal calibration framework for resilient integrated inertial systems, which overcomes the above issues, and enables both accurate and consistent calibration. Altogether four commonly employed sensors are supported iniKalibrcurrently, namely, IMU, radar, LiDAR, and camera. The proposed method starts with a rigorous and efficient dynamic initialization, where all parameters in the estimator would be accurately recovered. Subsequently, several continuous-time batch optimizations are conducted to refine the initialized parameters toward better states. Sufficient real-world experiments were conducted to verify the feasibility and evaluate the calibration performance ofiKalibr. The results demonstrate thatiKalibrcan achieve accurate resilient spatiotemporal calibration.

The rapid development of satellite technology has significantly enhanced satellite computing service capabilities, particularly in terms of its application potential for complex tasks such as hyperspectral image (HSI) processing. Satellite edge computing (SEC) substantially improves processing efficiency by transferring task processing to the satellite. At the same time, intelligent reflective surfaces (IRS) reduce the pressure on ground service center communication resources by optimizing communication links between satellites on the ground. However, existing works mainly optimize general computing tasks, resulting in limited performance when processing HSI tasks. This paper proposes an IRS-assisted HSI processing SEC system to achieve the optimal balance between HSI processing accuracy and system energy consumption. We formulate an optimization problem as a joint task covering HSI offloading, band selection, and IRS phase shift optimization to achieve optimal overall performance. To address the problem, we propose the joint feature iterative optimization (JFIO) framework for HSI processing, which generates optimized task offloading solutions through graph attention networks, utilizes multi-feature attention capsule networks to achieve efficient band selection, and combines this with IRS modules to optimize communication link conditions. Extensive experiments on various datasets demonstrate that the proposed framework achieves an excellent balance between accuracy and energy consumption, with its performance significantly outperforming other baseline methods.