A Lidar-Assisted Self-Localization Technology for Indoor Wireless Sensor Networks

The self-localization of wireless sensor networks (WSNs) is facing the problem of insufficient positioning accuracy in indoor environment due to multipath and interference issues. At the same time, without external references, through mutual measurement nodes can calculate only a set of relative coordinates. Therefore, it is difficult to achieve the mapping of coordinate values to the physical world. The development of simultaneous localization and mapping (SLAM) technologies has provided new opportunities to solve the above problems by making it easier to obtain real-time indoor maps. This article proposes a Lidar-assisted self-localization (LASL) technique to further improve the localization accuracy of WSNs in indoor scenes by combining spatial constraint information obtained from real-time maps, and to place the relative node coordinate network in the visualized maps. Based on the general assumption that the nodes are deployed on the surface of the object, the proposed technique combines the spatial constraints obtained by plane fitting of a local point cloud map (PCM) and finite-area approximation of object surfaces with the distance constraints provided by radio ranging. Subsequently, the self-localization results under the joint constraints are solved by the alternating coordinate descent (ACD) method. Simulations and experiments demonstrate that the proposed technique can effectively combine the spatial constraints provided by the Lidar PCM to further improve the self-localization accuracy of the sensor nodes, and further optimize the relative position relationship between each node and the map environment to achieve better matching and integration of the WSN and the real-time map.