Most visual–inertial navigation systems (VINSs) suffer from moving objects and achieve poor positioning accuracy in dynamic environments. Therefore, to improve the positioning accuracy of VINS in dynamic environments, a monocular visual–inertial navigation system, VINS-dimc, is proposed. This system integrates various constraints on the elimination of dynamic feature points, which helps to improve the positioning accuracy of VINSs in dynamic environments. First, the motion model, computed from the inertial measurement unit (IMU) data, is subjected to epipolar constraint and flow vector bound (FVB) constraint to eliminate feature matching that deviates significantly from the motion model. This algorithm then combines multiple feature point matching constraints that avoid the lack of single constraints and make the system more robust and universal. Finally, VINS-dimc was proposed, which can adapt to a dynamic environment. Experiments show that the proposed algorithm could accurately eliminate the dynamic feature points on moving objects while preserving the static feature points. It is a great help for the positioning accuracy and robustness of VINSs, whether they are from self-collected data or public datasets.

Remote sensing image is a vital basis for land management decisions. The protection of remote sensing images has seen the application of blockchain’s notarization function by many scholars. Yet, research on efficient retrieval of such images on the blockchain remains sparse. Addressing this issue, this paper introduces a blockchain-based spatial index verification method using Hyperledger Fabric. It linearizes the spatial information of remote sensing images via Geohash and integrates it with LSM trees for effective retrieval and verification. The system also incorporates IPFS as an underlying storage unit for Hyperledger Fabric, ensuring the safe storage and transmission of images. The experiments indicate that this method significantly reduces the latency in data retrieval and verification without impacting the write performance of Hyperledger Fabric, enhancing throughput and providing a solid foundation for efficient blockchain-based verification of remote sensing images in land registry systems.