# Collaborative Mobile Robotics for Semantic Mapping: A Survey
###### Abstract  
Ensuring safety in human-robot collaboration is one of the main challenges in mobile robotics today. Semantic maps are a potential solution because they provide semantic knowledge in addition to the geometric representation of the environment. They allow robots to perform their basic tasks using geometric representation, mainly localization, path planning and navigation, and additionally allow them to maintain a cognitive interpretation of the environment in order to reason and make decisions based on the context. The goal of this paper is to briefly review semantic mapping for a single mobile robot in indoor environments, and then focus on collaborative mobile semantic mapping. In both contexts, the semantic mapping process is divided into modules/tasks, and recent solutions for each module are discussed. Possible system architectures are also discussed for collaborative semantic mapping. Finally, future directions are highlighted. s +
Footnote †: journal: Medical Imaging  
emantic scene understanding; cooperating robots; data fusion; cognitive human-robot interaction  
Footnote †: journal: Medical Imaging  
There are a few previous works in the literature that have reviewed topics related to semantic mapping. The study [8] is one of the first reviews on semantic mapping. It focuses on highlighting its trends and main applications in indoor and outdoor scenes. Next, [PERSON] et al. [9] proposed a review focusing on visual semantic mapping. They focus on semantic-information extraction techniques based on visual data through feature extraction, object/place recognition and semantic representation methods. Subsequently, semantic mapping is discussed as one of many map representations in the more general review [10], which focuses on the history and trends of the simultaneous localization and mapping (SLAM) problem. In the recent review [11], the authors reviewed works on semantic mapping and focused on the application to semantic navigation, which involves performing navigation using high-level commands such as \"_go to the bathroom_\". Finally, the most recent study proposed in [12], reviewed the process of semantic mapping and focused on indoor scenes. It mainly presents semantic-mapping modules and different techniques to implement them, as well as current challenges and future directions. This review has four main differences from previous works. First, the main difference is that all previous works focus on reviewing single-robot semantic mapping, where a single robot is used for semantic mapping. However, recently, some works have started to address this problem in a collaborative mode using a fleet of robots. To the best of our knowledge, no previous work has examined collaborative semantic mapping. Indeed, there are works that have addressed collaborative geometric mapping, including collaborative SLAM [13], but not collaborative semantic mapping. Second, for the single-robot mode, this work focuses only on recent work on semantic mapping in indoor environments. Indeed, the perspective of this study is to use semantic mapping for successful human-robot interaction (HRI) in indoor environments, namely, domestic and industrial environments, so works interested in semantic mapping in outdoor environments [14; 15; 16] are not addressed in this paper, but there are some previous papers that have examined this topic [2; 8; 17]. For the collaborative mode, since there is much less work available, both indoor and outdoor semantic mapping are reviewed to give a better overview of what is carried out in the literature. Third, this work shows the main modules of the semantic-mapping process in single-robot and collaborative mode. It highlights the differences between them and the different solutions to implement these modules. The main contribution of this work is to explore the multi-robot data association and fusion module for the collaborative mode.