## 1 Introduction  
The accelerated evolution of Augmented Reality (AR) and Virtual Reality (VR) technologies has ushered in transformative possibilities across diverse domains. AR overlays digital information onto the real world, enhancing contextual understanding, while VR immerses users in entirely virtual environments. These technologies have demonstrated their prowess in education, healthcare, gaming, and beyond. Recent studies, such as the work of [PERSON] et al. [1], have dissected the nuances of user experiences, emphasizing the significance of tangible interfaces and motion controllers. [PERSON] et al. [2] highlighted the latency and errors in hand-tracking systems compared to traditional touch screens. [PERSON] et al. [3] explored stereo deficiencies in AR and VR headsets, revealing AR's superiority in user target selection due to heightened spatial awareness. While the existing literature delves into these differences, our study explores a common interface granting users comprehensive control over virtual objects designed and crafted on both AR and VR devices.  
AR/VR is increasingly used for human-robot interaction [4; 5; 6]. It can also facilitate human-augmented mapping with various use cases, including direct teleoperation [7],providing robot goals [8], aiding robotics application development [9], and visualizing robot intent [10]. Our study introduces a uniform interface enabling user control of virtual objects across an AR and a VR headset. This application aligns with human-augmented mapping [11] principles, actively involving users in the mapping process. Past works employed query-based human-in-the-loop systems, allowing users to rectify and inform robots about the semantic details of different regions using natural gestures [12]. Machine learning algorithms empower gesture recognition systems, infusing naturalism into interactions and expanding the scope of these technologies in computational science and engineering [13] and more specifically in human-robot interaction. We specifically focus on user experiences in semantic mapping, entailing the attachment of meaningful labels to sensory data for environment depiction.  
This paper presents a comparison study of the Microsoft Hololens 2 AR headset and the Meta Quest 2 VR headset on the same mapping task. An important difference between user experiences is how the user is able to manipulate the virtual objects: Hololens uses hand gestures and Quest uses controllers. Notably, the VR headset emulates AR through the Meta Quest 2's Passthrough feature, presenting users with grayscale surroundings. This emulation ensures controlled experiments, and our consistent environment facilitates in-depth analysis via a within-subjects user study. This study comprehensively compares the usability and feasibility of current AR and VR technologies in interactive tasks, including human-augmented semantic mapping. Specifically, we evaluate the usability, comfort, overall map accuracy, and task completion speed of various AR and VR headsets for creating and manipulating virtual objects and attaching labels to represent real-world tabletop objects. In particular, we investigate the usability and effectiveness of the different AR and VR headsets for a task in which the users manually create and manipulate virtual cuboid, spherical, and cylindrical volumes and attach labels to them to represent real-world tabletop objects. We explore which headset is easier to learn and use, more comfortable to use and wear, and which produces better overall map accuracy and faster task times. Our developed interfaces can also be extended to work on pre-generated autonomous semantic maps of larger room-scale environments. This comprehensive study contributes to understanding the capabilities and limitations of AR and VR technologies in enhancing user experiences in human-augmented semantic mapping scenarios.