Integrating Semantic Awareness and Probabilistic Priors for Object Search in Indoor Environments

Abstract

Semantic SLAM is defined as the extraction and integration of semantic awareness with geometric information to produce dense, multi-layered maps. Semantic maps are crucial for service robots to perform higher-level tasks while fostering human-robot interactions. Searching for objects is one such higher-level task that will become increasingly necessary for service robots. Object search involves the integration of scene-awareness with common-sense knowledge base. In this paper, we propose a method to combine our semantic map with probabilistic priors to find objects in an indoor environment. Our method also incorporates the ability to search for multiple objects during the task. In conjunction with our framework, we present the quantitative results of our experiments in an indoor environment.