This paper reviews state-of-the-art hashing-for-localization (HfL) technologies. It begins by reviewing visual localization and hashing-based visual retrieval technologies. In the field of visual localization, camera pose estimation and image geolocalization are two major research topics. Although their application areas differ, they share common key technologies in terms of image feature extraction and matching. Additionally, both face common challenges, including complex feature representation and slow localization speed. Hashing-based visual retrieval is a technology for accelerating retrieval using hashing algorithms. This technology performs feature matching by computing the Hamming distance between compact hash codes, which simplifies computational complexity and improves retrieval efficiency. Therefore, the development of hashing algorithms for accelerating visual localization represents an efficient and robust solution.

This paper introduces a series of HfL technologies that leverage hashing algorithms to accelerate visual localization. The typical process for implementing HfL begins by extracting visual features from a query image for localization and from reference images with localization information. These features are then mapped into compact binary hash codes using trained hash functions while preserving their visual similarities. During localization, the query image, represented by the generated hash code, is compared with the hash codes of the reference images in the database using the Hamming distance. Localization is achieved by selecting the closest matches through ranking, i.e., the localization information of the reference images whose hash codes have small Hamming distances to the query image’s hash code is considered the localization result. This paper reviews four types of HfL technologies: remote sensing HfL, sketch HfL, street view image geolocalization, and encrypting hashing against localization. It also discusses their empirical advantages over existing visual localization methods.

Through methodological and experimental analysis, it is observed that HfL offers the following three main advantages: (a) HfL significantly reduces storage requirements by mapping image features to compact binary hash codes; (b) HfL accelerates the feature matching process by computing the Hamming distance between hash codes; (c) HfL, aided by geographic cluster extraction, achieves exact localization. HfL effectively overcomes the inefficiencies of traditional visual localization technologies and represents a breakthrough in fast localization. In the future, fusing various data sources, including visual, global navigation satellite system, Wi-Fi, and acoustic signals, to enhance HfL will be a key trend. Leveraging large language models to update HfL with semantic information will be another key trend. HfL will provide efficient and accurate localization support across small-, medium-, and large-scale application scenarios.

Transmission of observational information through underwater acoustic channels is a critical aspect of testing underwater equipment. Traditional experimentation involving underwater observation and acoustic transmission is characterized by lengthy cycles and high costs. We aim to enhance experimental efficiency and reduce costs by simulating underwater acoustic channels on land to test the performance of underwater equipment from the observation end to the terminal. However, existing computer-based simulation methods struggle with direct physical integration with underwater equipment. To address this limitation, we aim to develop an embedded instrument to stimulate underwater acoustic channels, thereby facilitating onshore testing of underwater equipment.

To achieve this goal, we compile a database of underwater environmental parameters, including temperature, salinity, and topography. Using data from this database, we configured an underwater acoustic channel model tailored to a designated sea area. Our preferred model, Bellhop, derives from ray acoustics and can model acoustic channels in complicated underwater environments. Subsequently, we develop system software to operate the Bellhop model. In conjunction with the acoustic channel model, this software is adapted and embedded in a processing unit, specifically the chip RK3566, for our purposes. Additionally, we developed a circuit interface to establish a physical connection between the underwater equipment and the simulator. A user interface is also developed, which features configuration options, a display for the simulated channel, and an observation display. Through these innovations, we have developed an underwater acoustic channel simulator that facilitates direct physical connection to underwater equipment and offers flexible simulation of diverse sea areas. Onshore tests of the underwater equipment can be easily conducted by using an underwater acoustic channel simulator.

We conducted onshore tests of an underwater camera and its accompanying underwater acoustic communication scheme using the developed underwater acoustic channel simulator. This simulator was programmed to replicate the underwater acoustic channel conditions of a designated area in the Yellow Sea. The camera captured images and transmitted them through a simulated underwater acoustic channel to stimulate the process from the observation end to the terminal, with the images ultimately displayed by the simulator. This setup allowed us to assess the underwater equipment’s performance concerning image transmission quality over underwater acoustic communication. We obtained and analyzed images transmitted under varying conditions in the designated sea area, evaluating the performance of the underwater equipment and its acoustic communication scheme based on the quality of the images.

The introduction of the novel underwater acoustic channel simulator represents a significant advancement in testing methodologies for underwater equipment. This novel instrument facilitates experimental tests characterized by short cycles and low costs. Polit experiments can be efficiently conducted using the simulator to evaluate the performance of underwater equipment before conducting real-world underwater trials. Therefore, the underwater acoustic channel simulator emerges as a powerful tool for improving experimental test efficiency and reducing the costs associated with underwater equipment testing.

Underwater acoustic channels are normally limited by narrow bandwidth and are interfered by complicated environmental noise. These factors make high-efficiency and reliable underwater acoustic communication difficult to maintain. Semantic communication is characterized by microvolume representation and robust transmission. It could potentially improve underwater acoustic communication performance by overcoming narrow bandwidth limitation and complicated environmental noise interference. To validate the feasibility of revolutionizing underwater acoustic communication through semantic communication, an experimental simulation scheme of underwater acoustic semantic communication is developed.

This paper investigates and experimentally simulates data transmission through underwater acoustic channels based on semantic communication. The main process of the underwater acoustic semantic communication simulation scheme is presented as follows. Texts are the information source to be transmitted during the simulation. In the transmitter part, entities and relationships are extracted from texts using machine learning based on named entity recognition and relation extraction techniques. They form knowledge graphs. These knowledge graphs are concise semantic representations of the texts. In this process, knowledge graphs emerge as microvolume information carrier. This representation method does not require a large bandwidth of underwater acoustic channels to transmit data. In the receiver part, the texts are reconstructed from the knowledge graphs using machine learning based synonymous rephrasing techniques that are robust in noisy environments. The reconstructed texts are consistent with the originally transmitted texts at the semantic level. Therefore, efficient and reliable data transmission through underwater acoustic channels is possible in terms of semantically representing, transmitting, and reconstructing the data. To conduct the experimental simulations, a dataset comprising annotated Chinese descriptive texts for 520 underwater scenarios is established. Evaluation metrics include the number of transmitted bits, the bilingual evaluation understudy (BLEU) score, which measures word-level similarity, and the semantic similarity score, which measures sentence-level similarity. These metrics are used to evaluate the experimental results.

This paper compares underwater acoustic semantic communication experimental simulations with traditional underwater acoustic communication experimental simulations. The experimental results are analyzed as follows: 1) With an increase in the quantity of texts, the underwater acoustic semantic communication scheme consistently transmits fewer bits than traditional underwater acoustic communication scheme. 2) For the same transmitted texts, the BLEU score and semantic similarity score of the underwater acoustic semantic communication scheme remain generally stable within a low signal-to-noise ratio (SNR) range and demonstrate superior overall performance.

The experimental simulation results confirm that the underwater acoustic semantic communication scheme exhibits better data compression and stronger robustness in data transmission than the traditional underwater acoustic communication schemes. It extracts semantic information from the data, significantly reducing the transmitted data volume and decreasing the channel bandwidth requirements. In addition, data are reconstructed at the receiver part based on machine learning. This capability achieves robust data reconstruction that reduces the influence of complicated environmental noise. The underwater acoustic semantic communication scheme possesses technical advantages such as microvolume expression and reliable transmission, thus forming a new underwater acoustic communication baseline that overcomes the difficulties of narrow bandwidth and low SNR raised by underwater acoustic channels. Therefore, it offers a new strategy for revolutionizing underwater acoustic communication.