# Pathfinder for Low-altitude Aircraft with Binary Neural Network

arXiv: https://arxiv.org/abs/2409.08824 · HTML: https://mlsanity.com/paper/2409.08824/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 20 of its 47 references that are in the map.
- First posted on arXiv 13 Sep 2024.

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

- [Deep Compression: Compressing Deep Neural Networks with Pruning, Trained Quantization and Huffman Coding](https://mlsanity.com/paper/1510.00149/) (2015)
- [KPConv: Flexible and Deformable Convolution for Point Clouds](https://mlsanity.com/paper/1904.08889/) (2019)
- [XNOR-Net: ImageNet Classification Using Binary Convolutional Neural Networks](https://mlsanity.com/paper/1603.05279/) (2016)
- [RandLA-Net: Efficient Semantic Segmentation of Large-Scale Point Clouds](https://mlsanity.com/paper/1911.11236/) (2019)
- [SalsaNext: Fast, Uncertainty-aware Semantic Segmentation of LiDAR Point Clouds for Autonomous Driving](https://mlsanity.com/paper/2003.03653/) (2020)
- [Binary Neural Networks: A Survey](https://mlsanity.com/paper/2004.03333/) (2020)
- [ReActNet: Towards Precise Binary Neural Network with Generalized Activation Functions](https://mlsanity.com/paper/2003.03488/) (2020)
- [FAST-LIO2: Fast Direct LiDAR-inertial Odometry](https://mlsanity.com/paper/2107.06829/) (2021)
- [SensatUrban: Learning Semantics from Urban-Scale Photogrammetric Point Clouds](https://mlsanity.com/paper/2201.04494/) (2022)
- [BiViT: Extremely Compressed Binary Vision Transformer](https://mlsanity.com/paper/2211.07091/) (2022)
- [Binary DAD-Net: Binarized Driveable Area Detection Network for Autonomous Driving](https://mlsanity.com/paper/2006.08178/) (2020)
- [Stronger Together: Air-Ground Robotic Collaboration Using Semantics](https://mlsanity.com/paper/2206.14289/) (2022)
- [Efficient Urban-scale Point Clouds Segmentation with BEV Projection](https://mlsanity.com/paper/2109.09074/) (2021)
- [LACV-Net: Semantic Segmentation of Large-Scale Point Cloud Scene via Local Adaptive and Comprehensive VLAD](https://mlsanity.com/paper/2210.05870/) (2022)
- [Air-Ground Collaboration with SPOMP: Semantic Panoramic Online Mapping and Planning](https://mlsanity.com/paper/2407.09902/) (2024)
- [OpenStreetMap-based Autonomous Navigation With LiDAR Naive-Valley-Path Obstacle Avoidance](https://mlsanity.com/paper/2108.09117/) (2021)
- [Towards Energy Efficient Autonomous Exploration of Mars Lava Tube with a Martian Coaxial Quadrotor](https://mlsanity.com/paper/2211.06905/) (2022)
- [Imbalance Knowledge-Driven Multi-modal Network for Land-Cover Semantic Segmentation Using Images and LiDAR Point Clouds](https://mlsanity.com/paper/2303.15777/) (2023)
- [Multi-AGV's Temporal Memory-based RRT Exploration in Unknown Environment](https://mlsanity.com/paper/2207.07484/) (2022)
- [Active Loop Closure for OSM-guided Robotic Mapping in Large-Scale Urban Environments](https://mlsanity.com/paper/2407.17078/) (2024)

## Built on by (most cited first, 4 of 4)

- [BHViT: Binarized Hybrid Vision Transformer](https://mlsanity.com/paper/2503.02394/) (2025)
- [Information-Bottleneck Driven Binary Neural Network for Change Detection](https://mlsanity.com/paper/2507.03504/) (2025)
- [High-Fidelity Differential-information Driven Binary Vision Transformer](https://mlsanity.com/paper/2507.02222/) (2025)
- [Learning 1-Bit LiDAR-based Localization with Auxiliary Objective](https://mlsanity.com/paper/2606.27729/) (2026)

## Abstract (arXiv)

A prior global topological map (e.g., the OpenStreetMap, OSM) can boost the performance of autonomous mapping by a ground mobile robot. However, the prior map is usually incomplete due to lacking labeling in partial paths. To solve this problem, this paper proposes an OSM maker using airborne sensors carried by low-altitude aircraft, where the core of the OSM maker is a novel efficient pathfinder approach based on LiDAR and camera data, i.e., a binary dual-stream road segmentation model. Specifically, a multi-scale feature extraction based on the UNet architecture is implemented for images and point clouds. To reduce the effect caused by the sparsity of point cloud, an attention-guided gated block is designed to integrate image and point-cloud features. To optimize the model for edge deployment that significantly reduces storage footprint and computational demands, we propose a binarization streamline to each model component, including a variant of vision transformer (ViT) architecture as the encoder of the image branch, and new focal and perception losses to optimize the model training. The experimental results on two datasets demonstrate that our pathfinder method achieves SOTA accuracy with high efficiency in finding paths from the low-level airborne sensors, and we can create complete OSM prior maps based on the segmented road skeletons. Code and data are available at: hrefhttps://github.com/IMRL/Pathfinderhttps://github.com/IMRL/Pathfinder.