# SLOAM: Semantic Lidar Odometry and Mapping for Forest Inventory

arXiv: https://arxiv.org/abs/1912.12726 · HTML: https://mlsanity.com/paper/1912.12726/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Dec 2019.

## Built on by (most cited first, 17 of 17)

- [MULLS: Versatile LiDAR SLAM via Multi-metric Linear Least Square](https://mlsanity.com/paper/2102.03771/) (2021)
- [SA-LOAM: Semantic-aided LiDAR SLAM with Loop Closure](https://mlsanity.com/paper/2106.11516/) (2021)
- [Large-scale Autonomous Flight with Real-time Semantic SLAM under Dense Forest Canopy](https://mlsanity.com/paper/2109.06479/) (2021)
- [Greedy-Based Feature Selection for Efficient LiDAR SLAM](https://mlsanity.com/paper/2103.13090/) (2021)
- [TimberVision: A Multi-Task Dataset and Framework for Log-Component Segmentation and Tracking in Autonomous Forestry Operations](https://mlsanity.com/paper/2501.07360/) (2025)
- [Graph Neural Networks for Decentralized Multi-Agent Perimeter Defense](https://mlsanity.com/paper/2301.09689/) (2023)
- [SAGE-ICP: Semantic Information-Assisted ICP](https://mlsanity.com/paper/2310.07237/) (2023)
- [Vision-based Perimeter Defense via Multiview Pose Estimation](https://mlsanity.com/paper/2209.12136/) (2022)
- [Learning Decentralized Strategies for a Perimeter Defense Game with Graph Neural Networks](https://mlsanity.com/paper/2211.01757/) (2022)
- [Autonomous Advanced Aerial Mobility -- An End-to-end Autonomy Framework for UAVs and Beyond](https://mlsanity.com/paper/2311.04472/) (2023)
- [Forest Inspection Dataset for Aerial Semantic Segmentation and Depth Estimation](https://mlsanity.com/paper/2403.06621/) (2024)
- [QuadricsReg: Large-Scale Point Cloud Registration using Quadric Primitives](https://mlsanity.com/paper/2412.02998/) (2024)
- [Towards autonomous photogrammetric forest inventory using a lightweight under-canopy robotic drone](https://mlsanity.com/paper/2501.12073/) (2025)
- [AgriGS-SLAM: Orchard Mapping Across Seasons via Multi-View Gaussian Splatting SLAM](https://mlsanity.com/paper/2510.26358/) (2025)
- [Point Cloud based Hierarchical Deep Odometry Estimation](https://mlsanity.com/paper/2103.03394/) (2021)
- [Improved LiDAR Odometry and Mapping using Deep Semantic Segmentation and Novel Outliers Detection](https://mlsanity.com/paper/2403.03111/) (2024)
- [WildfireX-SLAM: A Large-scale Low-altitude RGB-D Dataset for Wildfire SLAM and Beyond](https://mlsanity.com/paper/2510.27133/) (2025)

## Abstract (arXiv)

This paper describes an end-to-end pipeline for tree diameter estimation based on semantic segmentation and lidar odometry and mapping. Accurate mapping of this type of environment is challenging since the ground and the trees are surrounded by leaves, thorns and vines, and the sensor typically experiences extreme motion. We propose a semantic feature based pose optimization that simultaneously refines the tree models while estimating the robot pose. The pipeline utilizes a custom virtual reality tool for labeling 3D scans that is used to train a semantic segmentation network. The masked point cloud is used to compute a trellis graph that identifies individual instances and extracts relevant features that are used by the SLAM module. We show that traditional lidar and image based methods fail in the forest environment on both Unmanned Aerial Vehicle (UAV) and hand-carry systems, while our method is more robust, scalable, and automatically generates tree diameter estimations.