# Radarize: Enhancing Radar SLAM with Generalizable Doppler-Based Odometry

arXiv: https://arxiv.org/abs/2311.11260 · HTML: https://mlsanity.com/paper/2311.11260/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 19 of its 51 references that are in the map.
- First posted on arXiv 19 Nov 2023.

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

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [ORB-SLAM2: an Open-Source SLAM System for Monocular, Stereo and RGB-D Cameras](https://mlsanity.com/paper/1610.06475/) (2016)
- [VINS-Mono: A Robust and Versatile Monocular Visual-Inertial State Estimator](https://mlsanity.com/paper/1708.03852/) (2017)
- [D3VO: Deep Depth, Deep Pose and Deep Uncertainty for Monocular Visual Odometry](https://mlsanity.com/paper/2003.01060/) (2020)
- [LIO-SAM: Tightly-coupled Lidar Inertial Odometry via Smoothing and Mapping](https://mlsanity.com/paper/2007.00258/) (2020)
- [RoNIN: Robust Neural Inertial Navigation in the Wild: Benchmark, Evaluations, and New Methods](https://mlsanity.com/paper/1905.12853/) (2019)
- [IONet: Learning to Cure the Curse of Drift in Inertial Odometry](https://mlsanity.com/paper/1802.02209/) (2018)
- [See Through Smoke: Robust Indoor Mapping with Low-cost mmWave Radar](https://mlsanity.com/paper/1911.00398/) (2019)
- [High Resolution Point Clouds from mmWave Radar](https://mlsanity.com/paper/2206.09273/) (2022)
- [VIODE: A Simulated Dataset to Address the Challenges of Visual-Inertial Odometry in Dynamic Environments](https://mlsanity.com/paper/2102.05965/) (2021)
- [RadarSLAM: Radar based Large-Scale SLAM in All Weathers](https://mlsanity.com/paper/2005.02198/) (2020)
- [Robust Real-time LiDAR-inertial Initialization](https://mlsanity.com/paper/2202.11006/) (2022)
- [Observability-Aware Intrinsic and Extrinsic Calibration of LiDAR-IMU Systems](https://mlsanity.com/paper/2205.03276/) (2022)
- [Radar Odometry for Autonomous Ground Vehicles: A Survey of Methods and Datasets](https://mlsanity.com/paper/2307.07861/) (2023)
- [AdaLIO: Robust Adaptive LiDAR-Inertial Odometry in Degenerate Indoor Environments](https://mlsanity.com/paper/2304.12577/) (2023)
- [BatMobility: Towards Flying Without Seeing for Autonomous Drones](https://mlsanity.com/paper/2307.11518/) (2023)
- [Online Self-Calibration for Visual-Inertial Navigation Systems: Models, Analysis and Degeneracy](https://mlsanity.com/paper/2201.09170/) (2022)

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

- [Event Camera Meets Mobile Embodied Perception: Abstraction, Algorithm, Acceleration, Application](https://mlsanity.com/paper/2503.22943/) (2025)
- [RaSCL: Radar to Satellite Crossview Localization](https://mlsanity.com/paper/2504.15899/) (2025)
- [RadarSplat-RIO: Indoor Radar-Inertial Odometry with Gaussian Splatting-Based Radar Bundle Adjustment](https://mlsanity.com/paper/2604.13492/) (2026)
- [RadarTwin: Scene-Specific mmWave Radar Simulation and Learning for Mobile Indoor Perception](https://mlsanity.com/paper/2606.28396/) (2026)
- [RaGNNarok: A Light-Weight Graph Neural Network for Enhancing Radar Point Clouds on Unmanned Ground Vehicles](https://mlsanity.com/paper/2507.00937/) (2025)
- [mmRadarTwin: A Measurement-Calibrated Signal-Level Digital Twin Platform for Indoor mmWave Radar](https://mlsanity.com/paper/2607.28108/) (2026)

## Abstract (arXiv)

Millimeter-wave (mmWave) radar is increasingly being considered as an alternative to optical sensors for robotic primitives like simultaneous localization and mapping (SLAM). While mmWave radar overcomes some limitations of optical sensors, such as occlusions, poor lighting conditions, and privacy concerns, it also faces unique challenges, such as missed obstacles due to specular reflections or fake objects due to multipath. To address these challenges, we propose Radarize, a self-contained SLAM pipeline that uses only a commodity single-chip mmWave radar. Our radar-native approach uses techniques such as Doppler shift-based odometry and multipath artifact suppression to improve performance. We evaluate our method on a large dataset of 146 trajectories spanning 4 buildings and mounted on 3 different platforms, totaling approximately 4.7 Km of travel distance. Our results show that our method outperforms state-of-the-art radar and radar-inertial approaches by approximately 5x in terms of odometry and 8x in terms of end-to-end SLAM, as measured by absolute trajectory error (ATE), without the need for additional sensors such as IMUs or wheel encoders.