Toward Autonomous Localization of Planetary Robotic Explorers by Relying on Semantic Mapping

Highly accurate localization of planetary robotic explorers is crucial for robust, efficient, and safe path planning in unknown and extreme planetary environments. In these environments, where satellite-based radio-navigation systems are unavailable, global localization can be achieved by relying on registration of ground imagery to an orbital map, X-band Doppler radio transmissions, or direct observation in satellite imagery. While these methods have proven to be effective, they rely heavily on a human-in-the-loop. This paper is concerned with autonomous global localization of planetary robotic explorers in extreme and GPS-denied environments by relying on semantic segmentation of ground imagery. Using a trained convolutional neural network (CNN), saliency maps are obtained from semantic segmentation of ground imagery. These maps are then registered to projected views of the terrain elevation maps in the rover's general region of operation to find the optimal match that places tight constraints on the pose of the robot in a Mars body-fixed coordinate system. We provide details on the use of the DeepLab V3+ framework for semantic image segmentation of Martian landscape imagery, including fine-tune training of existing models on domain specific data. Furthermore, we provide performance analysis of the proposed method on a Martian landscape dataset obtained by NASA's Perseverance rover, and discuss the limitations of the proposed method and future research directions.