EVALUATION AND COMPARISON OF DIFFERENT TIME OF FLIGHT CAMERAS FOR OUTDOOR APPLICATIONS

Abstract

Time-of-Flight (ToF) cameras have gained prominence in robotics, augmented reality, and gesture recognition due to their costeffective direct measurement of 3D environments. However, their outdoor applications remain limited, mainly due to challenges like sunlight interference. Through systematic testing under challenging outdoor conditions, we aim to assess the suitability of ToF cameras, specifically Azure Kinect and Pmd Monstar, in smart city contexts and contribute to the state-of-the-art and future trends of 3D sensing technology. Our experiments focus on three high-reflectivity cases: license plates, reflective road marking paint on cement and asphalt boards, and traffic cones. Results indicated that Azure Kinect offered a longer measurement range but was more susceptible to flying pixels. Pmd Monstar provided more stable depth measurements and was less sensitive to flying pixels. Differences in performance were attributed to their modulation frequencies and the distinct approaches to handling low-confidence points. By addressing the identified limitations and challenges, researchers and engineers can enhance ToF camera capabilities, ultimately improving their performance and expanding their applicability in outdoor transportation, autonomous driving, and other related smart city fields.