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contributor authorBaird, Christopher
contributor authorNokleby, Scott
date accessioned2025-04-21T10:31:19Z
date available2025-04-21T10:31:19Z
date copyright9/27/2024 12:00:00 AM
date issued2024
identifier issn2690-702X
identifier otherjavs_4_4_041001.pdf
identifier urihttp://yetl.yabesh.ir/yetl1/handle/yetl/4306365
description abstractAn algorithm is presented that intelligently merges multiple 3D point clouds used for localization based on when the point cloud was recorded to create an updated map that is more similar to the current environment. The algorithm was implemented on a Boston Dynamics Spot robot and was used to upgrade Spot’s autonomous navigation algorithm called Autowalk by adding the capability for long-term navigation in semi-static environments. The proposed algorithm was validated by having Spot navigate both indoor and outdoor environments over multiple months traveling over 43 km autonomously without losing localization. The proposed method extends the life of programmed autonomous missions to ensure a robot can be used over extended periods of time without the need to re-teach these autonomous missions due to changes in the environment.
publisherThe American Society of Mechanical Engineers (ASME)
titleEffective Map Merging for Long-Term Autonomous Navigation
typeJournal Paper
journal volume4
journal issue4
journal titleJournal of Autonomous Vehicles and Systems
identifier doi10.1115/1.4066517
journal fristpage41001-1
journal lastpage41001-11
page11
treeJournal of Autonomous Vehicles and Systems:;2024:;volume( 004 ):;issue: 004
contenttypeFulltext


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