# Pushbroom Stereo for High-Speed Navigation in Cluttered Environments

arXiv: https://arxiv.org/abs/1407.7091 · HTML: https://mlsanity.com/paper/1407.7091/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Jul 2014.

## Built on by (most cited first, 6 of 6)

- [StereoVoxelNet: Real-Time Obstacle Detection Based on Occupancy Voxels from a Stereo Camera Using Deep Neural Networks](https://mlsanity.com/paper/2209.08459/) (2022)
- [Generalization Evaluation of Deep Stereo Matching Methods for UAV-Based Forestry Applications](https://mlsanity.com/paper/2512.03427/) (2025)
- [Towards Gold-Standard Depth Estimation for Tree Branches in UAV Forestry: Benchmarking Deep Stereo Matching Methods](https://mlsanity.com/paper/2601.19461/) (2026)
- [Training Deep Stereo Matching Networks on Tree Branch Imagery: A Benchmark Study for Real-Time UAV Forestry Applications](https://mlsanity.com/paper/2602.19763/) (2026)
- [Progressive Per-Branch Depth Optimization for DEFOM-Stereo and SAM3 Joint Analysis in UAV Forestry Applications](https://mlsanity.com/paper/2602.20539/) (2026)
- [UE5-Forest: A Photorealistic Synthetic Stereo Dataset for UAV Forestry Depth Estimation](https://mlsanity.com/paper/2603.15304/) (2026)

## Abstract (arXiv)

We present a novel stereo vision algorithm that is capable of obstacle detection on a mobile-CPU processor at 120 frames per second. Our system performs a subset of standard block-matching stereo processing, searching only for obstacles at a single depth. By using an onboard IMU and state-estimator, we can recover the position of obstacles at all other depths, building and updating a full depth-map at framerate. Here, we describe both the algorithm and our implementation on a high-speed, small UAV, flying at over 20 MPH (9 m/s) close to obstacles. The system requires no external sensing or computation and is, to the best of our knowledge, the first high-framerate stereo detection system running onboard a small UAV.