# High-Speed Robot Navigation using Predicted Occupancy Maps

arXiv: https://arxiv.org/abs/2012.12142 · HTML: https://mlsanity.com/paper/2012.12142/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Dec 2020.

## Built on by (most cited first, 10 of 10)

- [Weakly-Supervised Multi-Granularity Map Learning for Vision-and-Language Navigation](https://mlsanity.com/paper/2210.07506/) (2022)
- [Learning Active Camera for Multi-Object Navigation](https://mlsanity.com/paper/2210.07505/) (2022)
- [Autonomous Driving Small-Scale Cars: A Survey of Recent Development](https://mlsanity.com/paper/2404.06229/) (2024)
- [Mobile Robot Navigation Using Hand-Drawn Maps: A Vision Language Model Approach](https://mlsanity.com/paper/2502.00114/) (2025)
- [Complex Terrain Navigation via Model Error Prediction](https://mlsanity.com/paper/2111.09768/) (2021)
- [Trans4Map: Revisiting Holistic Bird's-Eye-View Mapping from Egocentric Images to Allocentric Semantics with Vision Transformers](https://mlsanity.com/paper/2207.06205/) (2022)
- [Test-Time 3D Occupancy Prediction](https://mlsanity.com/paper/2503.08485/) (2025)
- [Map Imagination Like Blind Humans: Group Diffusion Model for Robotic Map Generation](https://mlsanity.com/paper/2412.16908/) (2024)
- [Map Prediction and Generative Entropy for Multi-Agent Exploration](https://mlsanity.com/paper/2501.13189/) (2025)
- [AquaBEV-Nav: Learned BEV Occupancy for Underwater Navigation and Exploration](https://mlsanity.com/paper/2609.32156/) (2026)

## Abstract (arXiv)

Safe and high-speed navigation is a key enabling capability for real world deployment of robotic systems. A significant limitation of existing approaches is the computational bottleneck associated with explicit mapping and the limited field of view (FOV) of existing sensor technologies. In this paper, we study algorithmic approaches that allow the robot to predict spaces extending beyond the sensor horizon for robust planning at high speeds. We accomplish this using a generative neural network trained from real-world data without requiring human annotated labels. Further, we extend our existing control algorithms to support leveraging the predicted spaces to improve collision-free planning and navigation at high speeds. Our experiments are conducted on a physical robot based on the MIT race car using an RGBD sensor where were able to demonstrate improved performance at 4 m/s compared to a controller not operating on predicted regions of the map.