Research On Key Technologies Of Intelligent Blind Guidance Equipment Based On Multisensor Information Fusion

China is the country with the largest number of blind people in the world,and the number of blind people has exceeded 8.3 million.To ensure the safety and reliability of blind people’s travel,environment modeling and path planning and obstacle avoidance techniques are becoming more and more important.For a series of problems that blind people may face in the actual travel process,this paper uses multi-sensor data fusion technology to design a smoother and safer 3D spatial path planning method,and the planned routes are more suitable for blind people to walk.Two kinds of 3D spatial environment map information acquisition methods,Li DAR and Kinect depth camera,are used to scan environmental objects at the same time,and obtain longdistance,sparse laser scan point clouds and close,dense depth camera point clouds respectively,and carry out coordinate conversion,radius filter-based denoising and iterative bilateral filterbased refinement and other pre-processing,and the pre-processed point clouds are more.The Iterative Closest Point(ICP)algorithm is used to align the laser scanning point cloud with the depth camera point cloud,and the 3D map is constructed by fusing the two point clouds.To address the problems that the conventional iterative closest point algorithm requires high initial position of the point cloud,slow convergence speed and easy to fall into local optimum,we propose an optimization improvement of the traditional ICP algorithm based on the consistency algorithm of feature point sampling to improve the alignment accuracy of the two systems of scanned point clouds.First,the two point clouds are divided into voxel grids,and the points of each grid are sampled to extract the feature points of the Li DAR point cloud and the depth camera point cloud,and then a Fast Point Feature Histogram(FPFH)is built to describe the feature points.Then,the coarse alignment of the two point clouds is performed using the Sample Consensus Initial Aligment(SAC-IA),and the initial coordinate transformation of the point clouds is calculated;finally,the ICP algorithm is improved by K-dimensional tree nearest neighbor search to complete the fine alignment of the two point clouds.The experimental results show that the proposed optimized ICP alignment method can effectively improve the alignment accuracy and convergence speed of the two point clouds.After acquiring the fused 3D point clouds from Li DAR and Kinect depth cameras,the 3D point clouds are converted into 3D spatial raster maps using an octree structure.Based on this3 D spatial raster map,three path planning methods,namely A-star,particle swarm algorithm and ant colony algorithm,were used to obtain the planned paths.The three planning paths were compared and fused to obtain the final optimized path as the planning path for walking in space for the blind,which improves the robustness of path planning and ensures the safety and efficiency of walking.