3D Scan Registration using Curvelet Features in Planetary Environments
Siddhant Ahuja, Peter Iles, Steven L. Waslander

TL;DR
This paper introduces a novel 3D scan registration method using curvelet features, which improves alignment accuracy in challenging planetary terrains with sparse, occluded data, outperforming existing techniques.
Contribution
The paper proposes a curvelet transform-based feature detection and matching approach for 3D scan registration in planetary environments, addressing limitations of traditional methods.
Findings
Improved registration accuracy in Mars-like terrain.
Effective feature detection in the curvelet domain.
Robust performance on real and simulated planetary data.
Abstract
Topographic mapping in planetary environments relies on accurate 3D scan registration methods. However, most global registration algorithms relying on features such as FPFH and Harris-3D show poor alignment accuracy in these settings due to the poor structure of the Mars-like terrain and variable resolution, occluded, sparse range data that is hard to register without some a-priori knowledge of the environment. In this paper, we propose an alternative approach to 3D scan registration using the curvelet transform that performs multi-resolution geometric analysis to obtain a set of coefficients indexed by scale (coarsest to finest), angle and spatial position. Features are detected in the curvelet domain to take advantage of the directional selectivity of the transform. A descriptor is computed for each feature by calculating the 3D spatial histogram of the image gradients, and nearest…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
