Accurate three-dimensional (3D) localization within indoor environments is crucial for enhancing item-based application services, yet current systems often struggle with localization accuracy and height estimation. This study introduces an advanced 3D localization system that integrates updated ultra-wideband (UWB) sensors and dual barometric pressure (BMP) sensors. Utilizing three fixed UWB anchors, the system employs geometric modeling and Kalman filtering for precise tag 3D spatial localization. Building on our previous research on indoor height measurement with dual BMP sensors, the proposed system demonstrates significant improvements in data processing speed and stability. Our enhancements include a new geometric localization model and an optimized Kalman filtering algorithm, which are validated by a high-precision motion capture system. The results show that the localization error is significantly reduced, with height accuracy of approximately ±0.05 m, and the Root Mean Square Error (RMSE) of the 3D localization system reaches 0.0740 m. The system offers expanded locatable space and faster data output rates, delivering reliable performance that supports advanced applications requiring detailed 3D indoor localization.

Accurate three-dimensional (3D) localization is critical for robust human-robot collaboration (HRC) in dynamic indoor environments. However, realizing high-precision localization in complex scenarios still faces challenges such as multipath effects, field-of-view occlusion, etc. To address these limitations, we propose Geo-LSTM, a geometry-constrained long short-term memory (LSTM) framework that integrates ultra-wideband (UWB) sensors, inertial measurement unit (IMU), and barometric pressure (BMP) sensors. First, a Simplified Geometric Localization (SGL) algorithm is proposed, which uses dual-BMP sensors and IMU sensor to obtain precise height information and utilizes the geometric relationships between the UWB tag and anchors to compute an initial location estimate, serving as a priori input for the Geo-LSTM network. This Geo-LSTM algorithm then incorporates multi-source geometric information to extract time-series features from the UWB ranging data and the tag's a priori location, further enhancing 3D localization accuracy. The experimental results from the cluttered indoor environments, including real-world HRC tasks with occlusions, show that the Geo-LSTM algorithm achieves an average 3D localization root mean square error (RMSE) of 0.103 m, representing improvements of 38.60% and 31.20% over the weighted least squares (WLS) method and the range-based LSTM algorithm, respectively. These results demonstrate Geo-LSTM's potential for reliable multi-sensor 3D localization in HRC applications.