A Biomimetic SLAM Algorithm Based on Growing Self-Organizing Map (GSOM-BSLAM), inspired by spatial cognitive mechanism of mammalian hippocampus, is proposed to resolve uncertainty problems in location identification and lack of real-time performance in simultaneous localization and mapping. The algorithm connects activation characteristics of the place cell and neurons in the output layer of the neural network to construct a topological map of space using a self-organizing growable mapping neural network. It utilizes self-motion-aware information to obtain activation response of the place cell to estimate the robot position information, improving the localization accuracy and real-time performance of the system. Meanwhile, an accurate environmental cognitive map is finally created by incorporating colordepth images for closed-loop detection and error correction for spatial cell path integration. The proposed algorithm is validated using publicly available KITTI and St. Lucia datasets. The experimental results demonstrate that the proposed algorithm outperforms RatSALM by 37.8% and 36.5% in terms of localization accuracy and real-time performance, respectively, indicating good mapping capabilities.

In order to promote vehicle active safety and to decrease collision accident, In this paper, an algorithm for detection of vehicle distance in front based on single and double camera switching is presented. This algorithm has done the corresponding difference processing to the day and night two different situations. By switching between single and double cameras, the image of the car bottom shadow, the characteristics of the vertical direction of the vehicle, the characteristics of the car taillight and other features was taken, and the position of the vehicle in front was locked. Finally, the feature information of the vehicle is converted into the distance information. Experiments show that, this system designed in the paper, achieved the anticipated function, can accurately detect multi-lane and short distance vehicle distance, and the efficiency of the system also achieved the real-time requirement.

To optimize the aerial ammunition scheduling and transportation, both the synergy among all departments of aerial ammunition support system and uncertain factors, such as traffic and attack situations, are taken into consideration in the model. The traffic and attack situations of roads are quantified as crossing time through the model so that the optimization can be simplified into multi-source shortest path problem. Then the optimal transport path of aerial ammunition and combination of storages can be determined by the improved Floyd-Warshall algorithm. Simulation result shows that the dynamic aerial ammunition scheduling and transportation model proposed in this paper is more suitable than traditional model in changing the decision according to the real-time fighting, so as to provide more reliable guarantee of troops.

LT code, as a channel coding scheme with good adaptability to the channel, has a stable performance in data transmission of underwater acoustic communication. In the case of large decoding overhead, the LT code can reach the ideal bit error rate(BER), but when the length of the encoded data is short, its coding and decoding performance is not ideal. LT code was applied to underwater acoustic communication by combining with orthogonal frequency division multiplexing (OFDM), and a LT-OFDM system was constructed. To improve the performance of LT code in the case of short code length, a method for degree distribution optimization was proposed on the objective of minimizing BER and minimizing average coding degree. Simulation and sea trial results show that under the same BER this method can optimize the system, transform 7%-22% conversion, and reduce the complexity of coding and decoding.

Aiming at the problems of low positioning accuracy and angle drift in the process of simultaneous localization and mapping (SLAM), inspired by the spatial cognitive mechanism of mammalian hippocampus, a bionic SLAM algorithm for constructing information conversion from multi-scale grid cell to place cell is proposed.Firstly, the proposed algorithm introduces head direction cell and stripe cell to perceive their own motion information while generating a multi-scale grid cell to cover the entire spatial environment, which can reduce the cumulative error due to angular offset. Secondly, as for the problem of low localization accuracy, the proposed algorithm uses a competitive neural network under Hebb learning rule to establish the information conversion relationship from multi-scale grid cell to place cell. Meanwhile, the mapping relationship between place cell and different landmarks in the spatial environment is constructed. Finally, the place cells with the maximum discharge rate are selected in order to form spatial cognitive topological map while realizing the autonomous localization of mobile robots. Compared with RatSLAM and ORB-SLAM2 on the KITTI public dataset, the results show that the proposed algorithm can realize autonomous localization and mapping in unknown environments by encoding the location information, while controlling the translation error at no more than 1.50 m and the rotation error at no higher than 1.0°.