# Large-scale Autonomous Flight with Real-time Semantic SLAM under Dense Forest Canopy

arXiv: https://arxiv.org/abs/2109.06479 · HTML: https://mlsanity.com/paper/2109.06479/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- Builds on 20 of its 42 references that are in the map.
- First posted on arXiv 14 Sep 2021.

## Builds on (most cited first, 20 of 20)

- [PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Space](https://mlsanity.com/paper/1706.02413/) (2017)
- [YOLOv3: An Incremental Improvement](https://mlsanity.com/paper/1804.02767/) (2018)
- [VoxelNet: End-to-End Learning for Point Cloud Based 3D Object Detection](https://mlsanity.com/paper/1711.06396/) (2017)
- [LIO-SAM: Tightly-coupled Lidar Inertial Odometry via Smoothing and Mapping](https://mlsanity.com/paper/2007.00258/) (2020)
- [CubeSLAM: Monocular 3D Object SLAM](https://mlsanity.com/paper/1806.00557/) (2018)
- [QuadricSLAM: Dual Quadrics from Object Detections as Landmarks in Object-oriented SLAM](https://mlsanity.com/paper/1804.04011/) (2018)
- [FAST-LIO: A Fast, Robust LiDAR-inertial Odometry Package by Tightly-Coupled Iterated Kalman Filter](https://mlsanity.com/paper/2010.08196/) (2020)
- [EGO-Planner: An ESDF-free Gradient-based Local Planner for Quadrotors](https://mlsanity.com/paper/2008.08835/) (2020)
- [Robust Stereo Visual Inertial Odometry for Fast Autonomous Flight](https://mlsanity.com/paper/1712.00036/) (2017)
- [Fast, Autonomous Flight in GPS-Denied and Cluttered Environments](https://mlsanity.com/paper/1712.02052/) (2017)
- [SLOAM: Semantic Lidar Odometry and Mapping for Forest Inventory](https://mlsanity.com/paper/1912.12726/) (2019)
- [Search-based Motion Planning for Aggressive Flight in SE(3)](https://mlsanity.com/paper/1710.02748/) (2017)
- [EGO-Swarm: A Fully Autonomous and Decentralized Quadrotor Swarm System in Cluttered Environments](https://mlsanity.com/paper/2011.04183/) (2020)
- [Monocular Camera Based Fruit Counting and Mapping with Semantic Data Association](https://mlsanity.com/paper/1811.01417/) (2018)
- [Search and Rescue under the Forest Canopy using Multiple UAVs](https://mlsanity.com/paper/1908.10541/) (2019)
- [Revisiting Boustrophedon Coverage Path Planning as a Generalized Traveling Salesman Problem](https://mlsanity.com/paper/1907.09224/) (2019)
- [Utilizing Semantic Visual Landmarks for Precise Vehicle Navigation](https://mlsanity.com/paper/1801.00858/) (2018)
- [Experiments in Fast, Autonomous, GPS-Denied Quadrotor Flight](https://mlsanity.com/paper/1806.07053/) (2018)
- [Semantic Mapping for Orchard Environments by Merging Two-Sides Reconstructions of Tree Rows](https://mlsanity.com/paper/1809.00075/) (2018)
- [The Open Vision Computer: An Integrated Sensing and Compute System for Mobile Robots](https://mlsanity.com/paper/1809.07674/) (2018)

## Built on by (most cited first, 12 of 12)

- [SPINE: Online Semantic Planning for Missions with Incomplete Natural Language Specifications in Unstructured Environments](https://mlsanity.com/paper/2410.03035/) (2024)
- [Air-Ground Collaboration for Language-Specified Missions in Unknown Environments](https://mlsanity.com/paper/2505.09108/) (2025)
- [A real-time dynamic obstacle tracking and mapping system for UAV navigation and collision avoidance with an RGB-D camera](https://mlsanity.com/paper/2209.08258/) (2022)
- [EvMAPPER: High Altitude Orthomapping with Event Cameras](https://mlsanity.com/paper/2409.18120/) (2024)
- [Deploying Foundation Model-Enabled Air and Ground Robots in the Field: Challenges and Opportunities](https://mlsanity.com/paper/2505.09477/) (2025)
- [Event Spectroscopy: Event-based Multispectral and Depth Sensing using Structured Light](https://mlsanity.com/paper/2509.06741/) (2025)
- [DeDelayed: Deleting Remote Inference Delay via On-Device Correction](https://mlsanity.com/paper/2510.13714/) (2025)
- [Analyzing General-Purpose Deep-Learning Detection and Segmentation Models with Images from a Lidar as a Camera Sensor](https://mlsanity.com/paper/2203.04064/) (2022)
- [Towards autonomous photogrammetric forest inventory using a lightweight under-canopy robotic drone](https://mlsanity.com/paper/2501.12073/) (2025)
- [ForestPersons: A Large-Scale Dataset for Under-Canopy Missing Person Detection](https://mlsanity.com/paper/2603.02541/) (2026)
- [Vision-Guided Outdoor Flight and Obstacle Evasion via Reinforcement Learning](https://mlsanity.com/paper/2605.24449/) (2026)
- [VoxelFix: Post-Hoc Semantic Correction of Completed 3D Voxel Maps](https://mlsanity.com/paper/2609.05114/) (2026)

## Abstract (arXiv)

Semantic maps represent the environment using a set of semantically meaningful objects. This representation is storage-efficient, less ambiguous, and more informative, thus facilitating large-scale autonomy and the acquisition of actionable information in highly unstructured, GPS-denied environments. In this letter, 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. The autonomous navigation module utilizes a multi-level planning and mapping framework and computes dynamically feasible trajectories that lead the UAV to build a semantic map of the user-defined region of interest in a computationally and storage efficient manner. 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. This leads the UAV to execute its mission accurately and safely at scale.