LaFlector: a Privacy-preserving LiDAR-based Approach for Accurate Indoor Tracking

Light Detection and Ranging (LiDAR) is used in various applications, from mapping the environment's topography to self-driving vehicles. Among such applications, the use of LiDAR for indoor tracking and quantifying visitors' interest (and ensuring safe distancing) is still not widely explored. Technologies based on wireless signals and video captures are typically used for indoor tracking, but they deficits concerning the lack of accuracy of captured signals or the lack of user privacy in the case of traditional surveillance cameras. Despite introducing tracking challenges inherent in the detection based on light reflection, LiDAR-based approaches represent a relatively low-cost solution for accurate indoor tracking. Thus, this paper presents LaFlector, a LiDAR-based indoor tracking system introducing tracking heuristics capable of detecting, classifying, and tracking several objects simultaneously, which are recorded and dynamically displayed in a 2D coordinate system. LaFlector was evaluated based on a low-cost 2D LiDAR hardware (Slamtec Mapper M1M1) and capable of detecting moving objects with high precision, showing in practice that a LiDAR-based can be used to track visitors' interest and count the number of moving objects.