Abstract
Autonomous mobile robotic teams are increasingly used in exploration of indoor environments. Accurate modeling of the world around the robot and describing the interaction of the robot with the world greatly increases the ability of the robot to act autonomously. This paper demonstrates the ability of autonomous robotic teams to find objects of interest. A novel feature of our approach is the object discovery and the use of it to augment the mapping and navigation process. The generated map can then be decomposed into into semantic regions while also considering the distance and line of sight to anchor points. The advantage of this approach is that the robot can return a dense map of the region around an object of interest. The robustness of this approach is demonstrated in indoor environments with multiple platforms with the objective of discovering objects of interest. 1. MOTIVATION Robots have shown a lot of potential in exploration, mapping and navigation of indoor environments. Robots are ideal for applications such as discovery of Improvised Explosive Devices (IEDs). Robotic teams can achieve this task faster, provide dense maps of regions around an object of interest and a model of the discovered object. The model of the discovered object can then be viewed by a human user and take the necessary actions. Semantic mapping and object discovery have gained interest independently. The intersection of these areas provides an interesting insight into using robots to discover objects of interest in indoor environments. Given the intended application, an object guided approach to indoor navigation is ideal. Robots can use knowledge of the environment and the semantic labeling to guide their ability to autonomously navigate and discover objects of interest. To achieve this, we propose an algorithm that combines the knowledge of discovered objects into the mapping process. This can provide an insight into the environment and aid the process of modeling the discovered objects.
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