# RoadRunner M&M -- Learning Multi-range Multi-resolution Traversability Maps for Autonomous Off-road Navigation

arXiv: https://arxiv.org/abs/2409.10940 · HTML: https://mlsanity.com/paper/2409.10940/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 25 of its 31 references that are in the map.
- First posted on arXiv 17 Sep 2024.

## Builds on (most cited first, 25 of 25)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks](https://mlsanity.com/paper/1905.11946/) (2019)
- [PointPillars: Fast Encoders for Object Detection from Point Clouds](https://mlsanity.com/paper/1812.05784/) (2018)
- [BEVFormer: Learning Bird's-Eye-View Representation from Multi-Camera Images via Spatiotemporal Transformers](https://mlsanity.com/paper/2203.17270/) (2022)
- [Lift, Splat, Shoot: Encoding Images From Arbitrary Camera Rigs by Implicitly Unprojecting to 3D](https://mlsanity.com/paper/2008.05711/) (2020)
- [BEVFusion: Multi-Task Multi-Sensor Fusion with Unified Bird's-Eye View Representation](https://mlsanity.com/paper/2205.13542/) (2022)
- [DETR3D: 3D Object Detection from Multi-view Images via 3D-to-2D Queries](https://mlsanity.com/paper/2110.06922/) (2021)
- [BEVFusion: A Simple and Robust LiDAR-Camera Fusion Framework](https://mlsanity.com/paper/2205.13790/) (2022)
- [Simple-BEV: What Really Matters for Multi-Sensor BEV Perception?](https://mlsanity.com/paper/2206.07959/) (2022)
- [BADGR: An Autonomous Self-Supervised Learning-Based Navigation System](https://mlsanity.com/paper/2002.05700/) (2020)
- [FB-BEV: BEV Representation from Forward-Backward View Transformations](https://mlsanity.com/paper/2308.02236/) (2023)
- [Information Theoretic Model Predictive Control: Theory and Applications to Autonomous Driving](https://mlsanity.com/paper/1707.02342/) (2017)
- [Fast Traversability Estimation for Wild Visual Navigation](https://mlsanity.com/paper/2305.08510/) (2023)
- [WayFAST: Navigation with Predictive Traversability in the Field](https://mlsanity.com/paper/2203.12071/) (2022)
- [V-STRONG: Visual Self-Supervised Traversability Learning for Off-road Navigation](https://mlsanity.com/paper/2312.16016/) (2023)
- [TerrainNet: Visual Modeling of Complex Terrain for High-speed, Off-road Navigation](https://mlsanity.com/paper/2303.15771/) (2023)
- [Locomotion Policy Guided Traversability Learning using Volumetric Representations of Complex Environments](https://mlsanity.com/paper/2203.15854/) (2022)
- [RoadRunner -- Learning Traversability Estimation for Autonomous Off-road Driving](https://mlsanity.com/paper/2402.19341/) (2024)
- [Self-Supervised Traversability Prediction by Learning to Reconstruct Safe Terrain](https://mlsanity.com/paper/2208.01329/) (2022)
- [EVORA: Deep Evidential Traversability Learning for Risk-Aware Off-Road Autonomy](https://mlsanity.com/paper/2311.06234/) (2023)
- [Learning Risk-Aware Costmaps via Inverse Reinforcement Learning for Off-Road Navigation](https://mlsanity.com/paper/2302.00134/) (2023)
- [Pixel to Elevation: Learning to Predict Elevation Maps at Long Range using Images for Autonomous Offroad Navigation](https://mlsanity.com/paper/2401.17484/) (2024)
- [WayFASTER: a Self-Supervised Traversability Prediction for Increased Navigation Awareness](https://mlsanity.com/paper/2402.00683/) (2024)
- [Lift-Attend-Splat: Bird's-eye-view camera-lidar fusion using transformers](https://mlsanity.com/paper/2312.14919/) (2023)
- [Deep Bayesian Future Fusion for Self-Supervised, High-Resolution, Off-Road Mapping](https://mlsanity.com/paper/2403.11876/) (2024)

## Built on by (most cited first, 5 of 5)

- [TartanGround: A Large-Scale Dataset for Ground Robot Perception and Navigation](https://mlsanity.com/paper/2505.10696/) (2025)
- [PhysGS: Bayesian-Inferred Gaussian Splatting for Physical Property Estimation](https://mlsanity.com/paper/2511.18570/) (2025)
- [Few-shot Semantic Learning for Robust Multi-Biome 3D Semantic Mapping in Off-Road Environments](https://mlsanity.com/paper/2411.06632/) (2024)
- [WildOS: Open-Vocabulary Object Search in the Wild](https://mlsanity.com/paper/2602.19308/) (2026)
- [ADEPT: Adaptive Diffusion Environment for Policy Transfer Sim-to-Real](https://mlsanity.com/paper/2506.01759/) (2025)

## Abstract (arXiv)

Autonomous robot navigation in off-road environments requires a comprehensive understanding of the terrain geometry and traversability. The degraded perceptual conditions and sparse geometric information at longer ranges make the problem challenging especially when driving at high speeds. Furthermore, the sensing-to-mapping latency and the look-ahead map range can limit the maximum speed of the vehicle. Building on top of the recent work RoadRunner, in this work, we address the challenge of long-range (100 m) traversability estimation. Our RoadRunner (M&M) is an end-to-end learning-based framework that directly predicts the traversability and elevation maps at multiple ranges (50 m, 100 m) and resolutions (0.2 m, 0.8 m) taking as input multiple images and a LiDAR voxel map. Our method is trained in a self-supervised manner by leveraging the dense supervision signal generated by fusing predictions from an existing traversability estimation stack (X-Racer) in hindsight and satellite Digital Elevation Maps. RoadRunner M&M achieves a significant improvement of up to 50% for elevation mapping and 30% for traversability estimation over RoadRunner, and is able to predict in 30% more regions compared to X-Racer while achieving real-time performance. Experiments on various out-of-distribution datasets also demonstrate that our data-driven approach starts to generalize to novel unstructured environments. We integrate our proposed framework in closed-loop with the path planner to demonstrate autonomous high-speed off-road robotic navigation in challenging real-world environments. Project Page: https://leggedrobotics.github.io/roadrunner_mm/