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Elevation Mapping for Locomotion and Navigation using GPU

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arxiv 2204.12876 v1 pith:RKW3ZG5X submitted 2022-04-27 cs.RO

classification cs.RO
keywords locomotionmappingelevationnavigationrobotsenvironmentexperimentsprocessing
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Perceiving the surrounding environment is crucial for autonomous mobile robots. An elevation map provides a memory-efficient and simple yet powerful geometric representation for ground robots. The robots can use this information for navigation in an unknown environment or perceptive locomotion control over rough terrain. Depending on the application, various post processing steps may be incorporated, such as smoothing, inpainting or plane segmentation. In this work, we present an elevation mapping pipeline leveraging GPU for fast and efficient processing with additional features both for navigation and locomotion. We demonstrated our mapping framework through extensive hardware experiments. Our mapping software was successfully deployed for underground exploration during DARPA Subterranean Challenge and for various experiments of quadrupedal locomotion.

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Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Parkour in the Wild: Learning a General and Extensible Agile Locomotion Policy Using Multi-expert Distillation and RL Fine-tuning

    cs.RO 2025-05 conditional novelty 7.0 of 10

    A single legged-robot policy, trained by distilling nine expert skills and then fine-tuning with reinforcement learning, matches or beats each expert and generalizes to unseen unstructured terrain.

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