DTIF: Robust Loop Closure Detection via Delaunay Triangle Topology in Complex Forests

2026-07-23

AI Summary

This work addresses the challenge of loop closure detection in GNSS-denied forest understory environments, where low-cost LiDAR point clouds are sparse and noisy, and repetitive tree arrangements with insufficient geometric distinctiveness often cause perceptual aliasing and false matches. To tackle this, the authors propose the DTIF framework, which introduces Delaunay triangulation for the first time to loop closure in forests, leveraging detected tree trunks as stable landmarks to construct a compact topological representation. Consistency verification based on edge lengths and circumradius statistics yields weighted vertex correspondences, while a decoupled robust pose estimation algorithm separately handles yaw rotation and horizontal/elevation translation. Evaluated on both simulated and real-world forest datasets, DTIF achieves high-accuracy, low-overhead global registration with strong robustness and computational efficiency, making it suitable for deployment on edge computing platforms.

Abstract

Accurate forest inventory and large-scale mapping are essential for ecosystem monitoring and sustainable forest management. Multiple low-cost edge platforms enable efficient large-area data acquisition, but merging independently constructed local maps in GNSS-denied understory environments still requires initialization-free loop closure detection and global registration. This task is challenging because low-cost LiDAR point clouds are sparse and noisy, while repetitive trunk layouts and the lack of distinctive geometric landmarks lead to severe perceptual aliasing and false correspondences. To address these issues, we propose DTIF (Delaunay Triangulation in Forests), a lightweight trunk-topology-based framework for forest loop closure detection and global registration. Tree trunks are first extracted as stable landmarks and encoded using a Delaunay topology for compact scene representation. Candidate submaps are then screened using edge-length and radius statistics, followed by edge--radius consistency verification and strong/weak vertex support aggregation to construct weighted vertex correspondences. Finally, topology-derived reliability weights are incorporated into a decoupled robust pose estimator that separately estimates yaw, horizontal translation, and elevation translation under gravity alignment. Experiments on simulated and real-world forest datasets demonstrate that DTIF achieves accurate registration with low computational overhead, providing a favorable balance among robustness, efficiency, and deployability on resource-constrained edge platforms.

Problem

• loop closure detection
• forest mapping
• perceptual aliasing
• LiDAR point clouds
• global registration

Innovation

• Delaunay triangulation
• loop closure detection
• forest mapping
• topology-based representation
• robust pose estimation