The intelligent road is an important component of the intelligent vehicle infrastructure cooperative system, the latest development of intelligent transportation systems. As an advanced sensor, Light Detection and Ranging (LiDAR) has gradually been used to collect high-resolution micro-traffic data on the roadside of intelligent roads. Furthermore, a fusion of multiple LiDARs has become a current hot spot to extend the data collection range and improve detection accuracy. This paper focuses on point cloud registration in a complex traffic environment and proposes a three-dimensional (3D) registration method based on traffic signs and prior knowledge of traffic scenes. Traffic signs with their reflective films are used as reference targets to register 3D point cloud data from roadside LiDARs. The proposed method consists of a vertical registration and a horizontal registration. For the vertical registration, we propose a panel rotation algorithm to rotate the initial point cloud to register it vertically, converting the 3D point cloud registration into a two-dimensional (2D) rigid body transformation. For the vertical registration, our system registers traffic signs from different LiDARs. Our method has been verified in some actual scenarios. Compared with previous methods, the proposed method is automatic and does not need to search reference targets manually. Furthermore, it is suitable for actual engineering use and can be applied to sparse point cloud data from LiDAR with few beams, realizing point cloud registration of large disparity.

Inspired by rodents’ free navigation through a specific space, RatSLAM mimics the function of the rat hippocampus to establish an environmental model within which the agent localizes itself. However, RatSLAM suffers from the deficiencies of erroneous loop-closure detection, low reliability on the experience map, and weak adaptability to environmental changes, such as lighting variation. To enhance environmental adaptability, this paper proposes an improved algorithm based on the HSI (hue, saturation, intensity) color space, which is superior in handling the characteristics of image brightness and saturation from the perspective of a biological visual model. The proposed algorithm first converts the raw image data from the RGB (red, green, blue) space into the HSI color space using a geometry derivation method. Then, a homomorphic filter is adopted to act on the I (intensity) channel and weaken the influence of the light intensity. Finally, guided filtering is used to process the S (saturation) channel and improve the significance of image details. The experimental results reveal that the improved RatSLAM model is superior to the original method in terms of the accuracy of visual template matching and robustness.

Traditional methods of respiration tracking used in radiosurgical robotics employ external optical markers to estimate the tumor position, which requires extracting the respiratory motion characteristics of the chest and establishing correlation models manually. The estimation is easily affected by the placement and number of markers. In order to solve the above problem, an estimation method of tumor location during respiratory motion is proposed using point clouds of the chest and abdominal skin surface. Based on the correlations with the tumor’s location, the essential area of the surface is selected as a data set and processed. Then, a hierarchical network is built to extract the feature of the skin and map those features to the location of tumors. In order to improve the estimation accuracy, a correlation smooth strategy is used to avoid the miss correlations between the skin surface and tumor locations. Investigations are conducted to find the optimal combinations of primary factors. Five typical respiratory data are collected in the experiments. Results show that combining the essential area of the skin surface and the classification network leads to better performance. Further results also show that the error of the proposed method is smaller than that of the traditional optical marker estimation method. Using the proposed method, manually extracting features and establishing correlation models are unnecessary, and the estimation accuracy is increased.

Inspired by rodents’ ability to navigate freely in a given space, bionavigation systems provide alternatives to traditional probabilistic solutions. This paper proposed a bionic path planning method based on RatSLAM to provide a novel viewpoint for robots to make a more flexible and intelligent navigation scheme. A neural network fusing historic episodic memory was proposed to improve the connectivity of the episodic cognitive map. It is biomimetically important to generate an episodic cognitive map and establish a one-to-one correspondence between the events generated by episodic memory and the visual template of RatSLAM. The episodic cognitive map can be improved by imitating the rodents’ behavior of memory fusion to produce better path planning results. The experimental results of different scenarios illustrate that the proposed method identified the connectivity between way points, optimized the result of path planning, and improved the flexibility of the system.

Stereotactic radiotherapy robots provide large benefits due to their high accuracy and stability. Using external features to correlate tumor motions is important for tumor tracking, a key technology in robotic radiosurgery. However, the correlations between external features extracted through skin surface movements and tumor motions vary in different thoracic and abdominal regions. External features with low correlation coefficients result in the degeneration of the chosen correlation model, thereby yielding a low fitting accuracy. To solve this problem, this letter proposes a method to extract highly representative surface motion features called essential surface motion. This method is based on selecting moving surface areas that are correlated with tumor motion according to high Pearson's correlation coefficients. The surface area is segmented into several regions before measuring their corresponding correlations with internal tumor motion. In consideration of the multidimensional structures of essential motion features, an improved correlation model based on the minimum cost function is proposed. Experimental results prove that our improved correlation model using essential surface motions as external features has a lower average error and absolute error than those that use surface marker motions.