Autonomous Navigation Using Active Visual-semantic SLAM

Abstract

The use of robots has become increasingly prevalent in nearly every industry, with robots found in not only manufacturing and transport, but also in healthcare and the home. This trend has likewise been accompanied by a demand for greater robot autonomy and the capacity to perform complex tasks unguided. In order to be able to do this, robots require the ability to perceive entities within the environment not only geometrically, but also semantically, as what an object is will dictate how it should be interacted with. Autonomous operation also requires that a robot be able to perform simultaneous localization and mapping (SLAM) within potentially unknown environments. In this thesis, Robot Operating System is used to implement a system which allows a robot to explore a previously unknown environment using a map generated by semantic SLAM. By extracting information from the map, the robot can be directed towards different semantic class instances found within the environment, with new instances found using frontier-based exploration. A custom terrain costmap layer is also created to enable semantics-aware path planning. The efficacy of these contributions are then demonstrated through experiments in a simulated environment