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A Deep Learning Approach to Hazard Detection for Autonomous Lunar Landing

Abstract

A deep learning approach is presented to detect safe landing locations using LIDAR scans of the Lunar surface. Semantic Segmentation is used to classify hazardous and safe locations from a LIDAR scan during the landing phase. Digital Elevation Maps from the Lunar Reconnaissance Orbiter mission are used to generate the training, validation, and testing dataset. The ground truth is generated using geometric techniques by evaluating the surface roughness, slope, and other hazard avoidance specifications. In order to train a robust model, artificially generated training data is augmented to the training dataset. A UNet-like neural network structure learns a lower dimensional representation of LIDAR scan to retain essential information regarding safety of the landing locations. A softmax activation layer at the bottom of the network ensures that the network outputs a probability of a safe landing spot. The network is also trained with a cost function that prioritizes the false safes to achieve a sub 1% false safes value. The results presented show the effectiveness of the technique for hazard detection. Future work on electing one landing spot based on proximity to the intended landing spot and the size of safety region around it is motivated.

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Correspondence to Rahul Moghe.

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This work is partially supported by NASA Johnson Space Center Grant NNX17AI35A.

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Moghe, R., Zanetti, R. A Deep Learning Approach to Hazard Detection for Autonomous Lunar Landing. J Astronaut Sci 67, 1811–1830 (2020). https://doi.org/10.1007/s40295-020-00239-8

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Keywords

  • Hazard detection
  • Machine learning
  • Autonomous landing
  • Semantic segmentation