# BatMobility: Towards Flying Without Seeing for Autonomous Drones

arXiv: https://arxiv.org/abs/2307.11518 · HTML: https://mlsanity.com/paper/2307.11518/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 19 of its 73 references that are in the map.
- First posted on arXiv 21 Jul 2023.

## Builds on (most cited first, 19 of 19)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [End-to-End Training of Deep Visuomotor Policies](https://mlsanity.com/paper/1504.00702/) (2015)
- [CAD2RL: Real Single-Image Flight without a Single Real Image](https://mlsanity.com/paper/1611.04201/) (2016)
- [Deep Drone Acrobatics](https://mlsanity.com/paper/2006.05768/) (2020)
- [EGO-Planner: An ESDF-free Gradient-based Local Planner for Quadrotors](https://mlsanity.com/paper/2008.08835/) (2020)
- [Learning to Fly by Crashing](https://mlsanity.com/paper/1704.05588/) (2017)
- [See Through Smoke: Robust Indoor Mapping with Low-cost mmWave Radar](https://mlsanity.com/paper/1911.00398/) (2019)
- [Deep Drone Racing: Learning Agile Flight in Dynamic Environments](https://mlsanity.com/paper/1806.08548/) (2018)
- [RRPN: Radar Region Proposal Network for Object Detection in Autonomous Vehicles](https://mlsanity.com/paper/1905.00526/) (2019)
- [AlphaPilot: Autonomous Drone Racing](https://mlsanity.com/paper/2005.12813/) (2020)
- [Pointillism: Accurate 3D bounding box estimation with multi-radars](https://mlsanity.com/paper/2203.04440/) (2022)
- [Learning Visuomotor Policies for Aerial Navigation Using Cross-Modal Representations](https://mlsanity.com/paper/1909.06993/) (2019)
- [VIODE: A Simulated Dataset to Address the Challenges of Visual-Inertial Odometry in Dynamic Environments](https://mlsanity.com/paper/2102.05965/) (2021)
- [Robust Vision-based Obstacle Avoidance for Micro Aerial Vehicles in Dynamic Environments](https://mlsanity.com/paper/2002.04920/) (2020)
- [Real-Time Trajectory Replanning for MAVs using Uniform B-splines and a 3D Circular Buffer](https://mlsanity.com/paper/1703.01416/) (2017)
- [3-D Motion Capture of an Unmodified Drone with Single-chip Millimeter Wave Radar](https://mlsanity.com/paper/2011.06730/) (2020)
- [SwarmControl: An Automated Distributed Control Framework for Self-Optimizing Drone Networks](https://mlsanity.com/paper/2005.09781/) (2020)
- [Obstacle Avoidance onboard MAVs using a FMCW RADAR](https://mlsanity.com/paper/2103.02050/) (2021)
- [A Low-Complexity Radar Detector Outperforming OS-CFAR for Indoor Drone Obstacle Avoidance](https://mlsanity.com/paper/2107.07250/) (2021)

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

- [Ultra-High-Frequency Harmony: mmWave Radar and Event Camera Orchestrate Accurate Drone Landing](https://mlsanity.com/paper/2502.14992/) (2025)
- [Radarize: Enhancing Radar SLAM with Generalizable Doppler-Based Odometry](https://mlsanity.com/paper/2311.11260/) (2023)
- [LightPure: Realtime Adversarial Image Purification for Mobile Devices Using Diffusion Models](https://mlsanity.com/paper/2409.00340/) (2024)
- [Curriculum-Based Reinforcement Learning for Autonomous UAV Navigation in Unknown Curved Tubular Conduit](https://mlsanity.com/paper/2512.10934/) (2025)

## Abstract (arXiv)

Unmanned aerial vehicles (UAVs) rely on optical sensors such as cameras and lidar for autonomous operation. However, such optical sensors are error-prone in bad lighting, inclement weather conditions including fog and smoke, and around textureless or transparent surfaces. In this paper, we ask: is it possible to fly UAVs without relying on optical sensors, i.e., can UAVs fly without seeing? We present BatMobility, a lightweight mmWave radar-only perception system for UAVs that eliminates the need for optical sensors. BatMobility enables two core functionalities for UAVs -- radio flow estimation (a novel FMCW radar-based alternative for optical flow based on surface-parallel doppler shift) and radar-based collision avoidance. We build BatMobility using commodity sensors and deploy it as a real-time system on a small off-the-shelf quadcopter running an unmodified flight controller. Our evaluation shows that BatMobility achieves comparable or better performance than commercial-grade optical sensors across a wide range of scenarios.