Robotic tower crane operation requires real-time perception of complex and rapidly changing construction environments. Conventional Simultaneous Localization and Mapping (SLAM) methods assume smooth sensor motion and emphasize geometry over semantics, limiting their suitability for crane-mounted sensing affected by vibration, rotation, and intermittent movement. This research proposes a multimodal perception framework that integrates Light Detection and Ranging (LiDAR), camera, and Inertial Measurement Unit (IMU) data within a tightly coupled fusion and semantic reconstruction pipeline. A Mahony-filter-based attitude optimization module stabilizes high-frequency vibrations, while a Fast LiDAR-Inertial Odometry (FAST-LIVO2)-inspired LiDAR–visual–inertial fusion strategy achieves centimeter-level three-dimensional (3D) mapping. To enhance scene understanding, an improved Random Sampled and Lightweight Aggregated Network (RandLA-Net) jointly exploits geometric and visual cues for point-level semantic segmentation, with color-aware spatial encoding. Field deployment on an operational tower crane demonstrates superior performance, yielding the lowest global reconstruction errors and highest semantic accuracy. The framework provides a robust perception foundation for autonomous planning, safety monitoring, and intelligent lifting assistance.

A brain–computer interface (BCI) establishes a direct communication pathway between the human brain and a computer. It has been widely used in medical diagnosis, rehabilitation, education, entertainment, and so on. Most research so far focuses on making BCIs more accurate and reliable, but much less attention has been paid to their privacy. Developing a commercial BCI system usually requires close collaborations among multiple organizations, e.g., hospitals, universities, and/or companies. Input data in BCIs, e.g., electroencephalogram (EEG), contain rich privacy information, and the developed machine learning model is usually proprietary. Data and model transmission among different parties may incur significant privacy threats, and hence, privacy protection in BCIs must be considered. Unfortunately, there does not exist any contemporary and comprehensive review on privacy-preserving BCIs. This article fills this gap, by describing potential privacy threats and protection strategies in BCIs. It also points out several challenges and future research directions in developing privacy-preserving BCIs.