DREAMS Laboratory · Arizona State University

Autonomous Terrain Mapping and Exploration

Simulation, next-best-view planning, and 3D reconstruction workflows for autonomous robotic mapping.

Research Context

At ASU's DREAMS Laboratory, my work focuses on autonomous mapping, simulation, and 3D reconstruction workflows. The research connects robotic viewpoint planning with digital-twin construction, using an RGB UAV in simulation to decide which camera views best improve reconstruction quality.

The submitted work, "Hybrid Sparse-Model Next-Best-View Planning for Active 3D Reconstruction for Digital Twins," plans directly from an incrementally updated Structure-from-Motion sparse point cloud rather than relying on a dense mesh, voxel map, or learned scene representation during flight.

Pipeline

The mission begins with a structured seed image capture around the target. After an initial reconstruction, candidate viewpoints are generated, filtered, scored, and selected based on the current sparse model.

Planning Strategies

The research compares co-visibility, baseline-aware repair, and hybrid next-best-view strategies that balance reconstruction improvement with viewpoint diversity, travel distance, and redundant observations.

Results Highlight

The best-performing oracle hybrid strategy achieved strong reconstruction fidelity on a simulated lunar rock task, including a 59.8 mm mean cloud-to-cloud error, 80% completeness at an 80 mm threshold, and 89% F-score at an 80 mm threshold.