Predicting Unobserved Space for Planning via Depth Map Augmentation Safe and efficient path planning is crucial for autonomous mobile robots. A prerequisite for path planning is to have a comprehensive understanding of the 3D structure of the robotu0027s environment. On Micro Air Vehicles (MAVs) this is commonly achieved using lowcost sensors, such as stereo or RGBD cameras. These sensors may fail to provide depth measurements in textureless or IRabsorbing areas and have limited effective range. In path planning, this results in inefficient trajectories or failure to recognize a feasible path to the goal, hence significantly impairing the robotu0027s mobility. Recent advances in deep learning enables us to exploit prior experience about the shape of the world and hence to infer complete depth maps from color images and additional sparse depth measurements. In this work, we present an augmented planning system and investigate the effects of employing stateoftheart depth completion techniques, specifically trained to augment sparse depth maps originating from RGBD sensors, semidense methods and stereo matchers. We extensively evaluate our approach in online path planning experiments based on simulated data, as well as global path planning experiments based on real world Micro Air Vehicle (MAV) data. We show that our augmented system, provided with only sparse depth perception, can reach onpar performance to ground truth depth input in simulated online planning experiments. On real world MAV data the augmented system demonstrates superior performance compared to a planner based on very dense RGBD depth maps.

Planning Beyond The Sensing Horizon Using a Learned Context Lastmile delivery systems commonly propose the use of autonomous robotic vehicles to increase scalability and efficiency. The economic inefficiency of collecting accurate prior maps for navigation motivates the use of planning algorithms that operate in unmapped environments. However, these algorithms typically waste time exploring regions that are unlikely to contain the delivery destination. Context is key information about structured environments that could guide exploration toward the unknown goal location, but the abstract idea is difficult to quantify for use in a planning algorithm. Some approaches specifically consider contextual relationships between objects, but would perform poorly in objectsparse environments like outdoors. Recent deep learningbased approaches consider context too generally, making trainingtransferability difficult. Therefore, this work proposes a novel formulation of utilizing context for planning as an imagetoimage translation problem, which is shown to extract terrain context from semantic gridmaps, into a metric that an explorationbased planner can use. The proposed framework has the benefit of training on a static dataset instead of requiring a timeconsuming simulator. Across 42 test houses with layouts from satellite images, the trained algorithm enables a robot to reach its goal 189% faster than with a contextunaware planner, and within 63% of the optimal path computed with a prior map. The proposed algorithm is also implemented on a vehicle with a forwardfacing camera in a highfidelity, Unreal simulation of neighborhood houses.