most citedNeural Elevation Models for Terrain Mapping and Path Planning

1 citations · 1 across the 5 of their papers we have counts for

collaborators

6 papers

cs.RO2026

End-to-End Vision-Language-Action Policies for Autonomous Lunar Rover Docking

Sitan Huang, Grace Gao

Surface navigation on the Moon is subject to extreme illumination contrast and deployment-time perceptual disturbances. While modular navigation pipelines have long supported plane…

cs.CV2026

Coverage Optimization for Camera View Selection

Timothy Chen, Adam Dai, Maximilian Adang +2

What makes a good viewpoint? The quality of the data used to learn 3D reconstructions is crucial for enabling efficient and accurate scene modeling. We study the active view select…

cs.CV2026

Semantic Segmentation and Depth Estimation for Real-Time Lunar Surface Mapping Using 3D Gaussian Splatting

Guillem Casadesus Vila, Adam Dai, Grace Gao

Navigation and mapping on the lunar surface require robust perception under challenging conditions, including poorly textured environments, high-contrast lighting, and limited comp…

cs.RO2026

Neural Radiance Maps for Extraterrestrial Navigation and Path Planning

Adam Dai, Shubh Gupta, Grace Gao

Autonomous vehicles such as the Mars rovers currently lead the vanguard of surface exploration on extraterrestrial planets and moons. In order to accelerate the pace of exploration…

cs.RO2026

Visual SLAM with DEM Anchoring for Lunar Surface Navigation

Adam Dai, Guillem Casadesus Vila, Grace Gao

Future lunar missions will require autonomous rovers capable of traversing tens of kilometers across challenging terrain while maintaining accurate localization and producing globa…

cs.RO2024★ 1 cited

Neural Elevation Models for Terrain Mapping and Path Planning

Adam Dai, Shubh Gupta, Grace Gao

This work introduces Neural Elevations Models (NEMos), which adapt Neural Radiance Fields to a 2.5D continuous and differentiable terrain model. In contrast to traditional terrain…