Mesh Robotics, LLC — National Aeronautics and Space Administration STTR Phase I: T4

Description

<p>We propose to develop a method to effectively utilize the massive amount of image and range data that cameras and laser scanners can generate for an autonomous navigation system. &nbsp;<strong>Our innovative approach is to use deep learning to detect and segment only the most useful portions of the data and to use that to build better 3D models. </strong> We have proven methods for building accurate 3D models of the environment for robotic systems. [Wettergreen12] This new work will enable us to create better 3D models by identifying and incorporating the most salient information. &nbsp;These models will be more sparse but will have higher information content. This will improve ease of communication and quality of action planning.</p><p>In Phase 1 we will prove the underlying concepts (use of salient features, segmentation by learning, efficient 3D modeling) in the context of one test environment. &nbsp;We will evaluate methods for determining the importance of object characteristics in their overall quality as a landmark. We will demonstrate improved landmark detection and selection in rocky and natural terrain. In Phase 2 we will generalize the work, considering several policies for salience and will implement system and test in multiple environments. &nbsp;We will demonstrate system learning over time and detecting new and reliable landmarks. Evaluate and demonstrate selective downlink methods that allow for offline training while also returning science-relevant data.</p>