The paper shows that monocular depth estimation from foundation models, enhanced with SLAM rescaling, edge masking, and temporal smoothing, can match LiDAR for off-road robot navigation.
arXiv preprint arXiv:2501.18942 , year=
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An open-sourced Unified Autonomy Stack fuses LiDAR, radar, vision and inertial data with sampling-based planning and control barrier functions to deliver resilient autonomy on aerial and ground robots in challenging real-world settings.
citing papers explorer
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An Open-Source LiDAR and Monocular Off-Road Autonomous Navigation Stack
The paper shows that monocular depth estimation from foundation models, enhanced with SLAM rescaling, edge masking, and temporal smoothing, can match LiDAR for off-road robot navigation.
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The Unified Autonomy Stack: Toward a Blueprint for Generalizable Robot Autonomy
An open-sourced Unified Autonomy Stack fuses LiDAR, radar, vision and inertial data with sampling-based planning and control barrier functions to deliver resilient autonomy on aerial and ground robots in challenging real-world settings.