TreeLoc: 6-DoF LiDAR Global Localization in Forests via Inter-Tree Geometric Matching
- URL: http://arxiv.org/abs/2602.01501v2
- Date: Tue, 03 Feb 2026 08:23:36 GMT
- Title: TreeLoc: 6-DoF LiDAR Global Localization in Forests via Inter-Tree Geometric Matching
- Authors: Minwoo Jung, Nived Chebrolu, Lucas Carvalho de Lima, Haedam Oh, Maurice Fallon, Ayoung Kim,
- Abstract summary: We propose TreeLoc, a LiDAR-based global localization framework for forests.<n>On diverse forest benchmarks, TreeLoc outperforms baselines, achieving precise localization.<n>We also propose applications for long-term forest management using descriptors from a compact global tree database.
- Score: 7.581546372433995
- License: http://creativecommons.org/licenses/by-nc-nd/4.0/
- Abstract: Reliable localization is crucial for navigation in forests, where GPS is often degraded and LiDAR measurements are repetitive, occluded, and structurally complex. These conditions weaken the assumptions of traditional urban-centric localization methods, which assume that consistent features arise from unique structural patterns, necessitating forest-centric solutions to achieve robustness in these environments. To address these challenges, we propose TreeLoc, a LiDAR-based global localization framework for forests that handles place recognition and 6-DoF pose estimation. We represent scenes using tree stems and their Diameter at Breast Height (DBH), which are aligned to a common reference frame via their axes and summarized using the tree distribution histogram (TDH) for coarse matching, followed by fine matching with a 2D triangle descriptor. Finally, pose estimation is achieved through a two-step geometric verification. On diverse forest benchmarks, TreeLoc outperforms baselines, achieving precise localization. Ablation studies validate the contribution of each component. We also propose applications for long-term forest management using descriptors from a compact global tree database. TreeLoc is open-sourced for the robotics community at https://github.com/minwoo0611/TreeLoc.
Related papers
- Learning Order Forest for Qualitative-Attribute Data Clustering [52.612779710298526]
This paper discovers a tree-like distance structure to flexibly represent the local order relationship among intra-attribute qualitative values.<n>A joint learning mechanism is proposed to iteratively obtain more appropriate tree structures and clusters.<n>Experiments demonstrate that the joint learning adapts the forest to the clustering task to yield accurate results.
arXiv Detail & Related papers (2026-03-03T07:49:50Z) - Fast Inference of Visual Autoregressive Model with Adjacency-Adaptive Dynamical Draft Trees [50.230925890958936]
We propose an adjacency-adaptive dynamic draft tree that adjusts draft tree depth and width by leveraging adjacent token states and prior acceptance rates.<n>ADT-Tree achieves speedups of 3.13xand 3.05x, respectively, and integrates seamlessly with relaxed sampling methods such as LANTERN.
arXiv Detail & Related papers (2025-12-26T04:45:49Z) - ForestFormer3D: A Unified Framework for End-to-End Segmentation of Forest LiDAR 3D Point Clouds [0.06282171844772422]
We present ForestFormer3D, a new unified and end-to-end framework for precise individual tree and semantic segmentation.<n>ForestFormer3D incorporates ISA-guided query point selection, a score-based block merging strategy during inference, and a one-to-many association mechanism for effective training.<n>Our model achieves state-of-the-art performance for individual tree segmentation on the newly introduced FOR-instanceV2 dataset.
arXiv Detail & Related papers (2025-06-20T13:39:27Z) - ForestLPR: LiDAR Place Recognition in Forests Attentioning Multiple BEV Density Images [38.727720300337296]
We propose a robust LiDAR-based place recognition method for natural forests, ForestLPR.<n>Cross-sectional images of the forest's geometry at different heights contain the information needed to recognize revisiting a place.<n>Our approach utilizes a visual transformer as the shared backbone to produce sets of local descriptors.
arXiv Detail & Related papers (2025-03-06T14:24:22Z) - ReTreever: Tree-based Coarse-to-Fine Representations for Retrieval [64.44265315244579]
We propose a tree-based method for organizing and representing reference documents at various granular levels.<n>Our method, called ReTreever, jointly learns a routing function per internal node of a binary tree such that query and reference documents are assigned to similar tree branches.<n>Our evaluations show that ReTreever generally preserves full representation accuracy.
arXiv Detail & Related papers (2025-02-11T21:35:13Z) - NeRF-Accelerated Ecological Monitoring in Mixed-Evergreen Redwood Forest [0.0]
We present a comparison of MLS and NeRF forest reconstructions for the purpose of trunk diameter estimation in a mixed-evergreen Redwood forest.
We propose an improved DBH-estimation method using convex-hull modeling.
arXiv Detail & Related papers (2024-10-09T20:32:15Z) - Forecasting with Hyper-Trees [50.72190208487953]
Hyper-Trees are designed to learn the parameters of time series models.
By relating the parameters of a target time series model to features, Hyper-Trees also address the issue of parameter non-stationarity.
In this novel approach, the trees first generate informative representations from the input features, which a shallow network then maps to the target model parameters.
arXiv Detail & Related papers (2024-05-13T15:22:15Z) - Learning a Decision Tree Algorithm with Transformers [75.96920867382859]
We introduce MetaTree, a transformer-based model trained via meta-learning to directly produce strong decision trees.
We fit both greedy decision trees and globally optimized decision trees on a large number of datasets, and train MetaTree to produce only the trees that achieve strong generalization performance.
arXiv Detail & Related papers (2024-02-06T07:40:53Z) - AdaTreeFormer: Few Shot Domain Adaptation for Tree Counting from a Single High-Resolution Image [11.649568595318307]
This paper proposes a framework that is learnt from the source domain with sufficient labeled trees.
It is adapted to the target domain with only a limited number of labeled trees.
Experimental results show that AdaTreeFormer significantly surpasses the state of the art.
arXiv Detail & Related papers (2024-02-05T12:34:03Z) - TreeFormer: a Semi-Supervised Transformer-based Framework for Tree
Counting from a Single High Resolution Image [6.789370732159176]
Tree density estimation and counting using single aerial and satellite images is a challenging task in photogrammetry and remote sensing.
We propose the first semisupervised transformer-based framework for tree counting which reduces the expensive tree annotations for remote sensing images.
Our model was evaluated on two benchmark tree counting datasets, Jiangsu, and Yosemite, as well as a new dataset, KCL-London, created by ourselves.
arXiv Detail & Related papers (2023-07-12T12:19:36Z) - Large-scale Autonomous Flight with Real-time Semantic SLAM under Dense
Forest Canopy [48.51396198176273]
We propose an integrated system that can perform large-scale autonomous flights and real-time semantic mapping in challenging under-canopy environments.
We detect and model tree trunks and ground planes from LiDAR data, which are associated across scans and used to constrain robot poses as well as tree trunk models.
A drift-compensation mechanism is designed to minimize the odometry drift using semantic SLAM outputs in real time, while maintaining planner optimality and controller stability.
arXiv Detail & Related papers (2021-09-14T07:24:53Z) - Growing Deep Forests Efficiently with Soft Routing and Learned
Connectivity [79.83903179393164]
This paper further extends the deep forest idea in several important aspects.
We employ a probabilistic tree whose nodes make probabilistic routing decisions, a.k.a., soft routing, rather than hard binary decisions.
Experiments on the MNIST dataset demonstrate that our empowered deep forests can achieve better or comparable performance than [1],[3].
arXiv Detail & Related papers (2020-12-29T18:05:05Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.