High-precision indoor localization is growing extremely quickly, especially for multi-floor scenarios. The data on existing indoor positioning schemes, mainly, come from wireless, visual, or lidar means, which are limited to a single sensor. With the massive deployment of WiFi access points and low-cost cameras, it is possible to combine the above three methods to achieve more accurate, complete, and reliable location results. However, the existing SLAM rapidly advances, so hybrid visual and wireless approaches take advantage of this, in a straightforward manner, without exploring their interactions. In this paper, a high-precision multi-floor indoor positioning method, based on vision, wireless signal characteristics, and lidar is proposed. In the joint scheme, we, first, use the positioning data output in lidar SLAM as the theoretical reference position for visual images; then, use a WiFi signal to estimate the rough area, with likelihood probability; and, finally, use the visual image to fine-tune the floor-estimation and location results. Based on the numerical results, we show that the proposed joint localization scheme can achieve 0.62 m of 3D localization accuracy, on average, and a 1.24-m MSE for two-dimensional tracking trajectories, with an estimation accuracy for the floor equal to 89.22%. Meanwhile, the localization process takes less than 0.25 s, which is of great importance for practical implementation.

High-precision indoor positioning based on fingerprint usually requires more observation samples, either time or frequency. Our previous work explores the possibility of enlarging the observation window in the spatial domain, which requires significant baseband and radio frequency (RF) processing capabilities to deal with multiple antennas. In this paper, we propose a low-complexity antenna selection-based positioning scheme in order to trade off the positioning performance and hardware cost or computational complexity. The optimal antenna subset is selected with the help of the theoretical error-bound analysis, which ensures robust and high-precision positioning performance. Based on the numerical results, we show that our proposed antenna selection mechanism is able to reserve the positioning precision improvement provided by the spatial domain observations and reduce more than 100-200 times implementation complexity in terms of running times, if compared with other baseline selection algorithms.