# NTU4DRadLM: 4D Radar-centric Multi-Modal Dataset for Localization and Mapping

arXiv: https://arxiv.org/abs/2309.00962 · HTML: https://mlsanity.com/paper/2309.00962/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 40 references that are in the map.
- First posted on arXiv 2 Sep 2023.

## Builds on (most cited first, 11 of 11)

- [VINS-Mono: A Robust and Versatile Monocular Visual-Inertial State Estimator](https://mlsanity.com/paper/1708.03852/) (2017)
- [LIO-SAM: Tightly-coupled Lidar Inertial Odometry via Smoothing and Mapping](https://mlsanity.com/paper/2007.00258/) (2020)
- [K-Radar: 4D Radar Object Detection for Autonomous Driving in Various Weather Conditions](https://mlsanity.com/paper/2206.08171/) (2022)
- [TJ4DRadSet: A 4D Radar Dataset for Autonomous Driving](https://mlsanity.com/paper/2204.13483/) (2022)
- [FAST-LIO: A Fast, Robust LiDAR-inertial Odometry Package by Tightly-Coupled Iterated Kalman Filter](https://mlsanity.com/paper/2010.08196/) (2020)
- [Raw High-Definition Radar for Multi-Task Learning](https://mlsanity.com/paper/2112.10646/) (2021)
- [ColoRadar: The Direct 3D Millimeter Wave Radar Dataset](https://mlsanity.com/paper/2103.04510/) (2021)
- [4D iRIOM: 4D Imaging Radar Inertial Odometry and Mapping](https://mlsanity.com/paper/2303.13962/) (2023)
- [R2LIVE: A Robust, Real-time, LiDAR-Inertial-Visual tightly-coupled state Estimator and mapping](https://mlsanity.com/paper/2102.12400/) (2021)
- [Resource and Response Aware Path Planning for Long-term Autonomy of Ground Robots in Agriculture](https://mlsanity.com/paper/2105.10690/) (2021)
- [Continuous-time Radar-inertial Odometry for Automotive Radars](https://mlsanity.com/paper/2201.02437/) (2022)

## Built on by (most cited first, 17 of 17)

- [PLGSLAM: Progressive Neural Scene Represenation with Local to Global Bundle Adjustment](https://mlsanity.com/paper/2312.09866/) (2023)
- [RadarOcc: Robust 3D Occupancy Prediction with 4D Imaging Radar](https://mlsanity.com/paper/2405.14014/) (2024)
- [Exploring Radar Data Representations in Autonomous Driving: A Comprehensive Review](https://mlsanity.com/paper/2312.04861/) (2023)
- [Doracamom: Joint 3D Detection and Occupancy Prediction with Multi-view 4D Radars and Cameras for Omnidirectional Perception](https://mlsanity.com/paper/2501.15394/) (2025)
- [RIDERS: Radar-Infrared Depth Estimation for Robust Sensing](https://mlsanity.com/paper/2402.02067/) (2024)
- [HeRCULES: Heterogeneous Radar Dataset in Complex Urban Environment for Multi-session Radar SLAM](https://mlsanity.com/paper/2502.01946/) (2025)
- [4D mmWave Radar for Sensing Enhancement in Adverse Environments: Advances and Challenges](https://mlsanity.com/paper/2503.24091/) (2025)
- [4D-ROLLS: 4D Radar Occupancy Learning via LiDAR Supervision](https://mlsanity.com/paper/2505.13905/) (2025)
- [USVTrack: USV-Based 4D Radar-Camera Tracking Dataset for Autonomous Driving in Inland Waterways](https://mlsanity.com/paper/2506.18737/) (2025)
- [LEROjD: Lidar Extended Radar-Only Object Detection](https://mlsanity.com/paper/2409.05564/) (2024)
- [Equi-RO: A 4D mmWave Radar Odometry via Equivariant Networks](https://mlsanity.com/paper/2509.20674/) (2025)
- [DIDLM: A SLAM Dataset for Difficult Scenarios Featuring Infrared, Depth Cameras, LIDAR, 4D Radar, and Others under Adverse Weather, Low Light Conditions, and Rough Roads](https://mlsanity.com/paper/2404.09622/) (2024)
- [Registering the 4D Millimeter Wave Radar Point Clouds Via Generalized Method of Moments](https://mlsanity.com/paper/2508.02187/) (2025)
- [Rad-GS: Radar-Vision Integration for 3D Gaussian Splatting SLAM in Outdoor Environments](https://mlsanity.com/paper/2511.16091/) (2025)
- [4DRaL: Bridging 4D Radar with LiDAR for Place Recognition using Knowledge Distillation](https://mlsanity.com/paper/2603.26206/) (2026)
- [4D Radar Perception Algorithms for Autonomous Driving: A Review](https://mlsanity.com/paper/2609.19216/) (2026)
- [RCVLA: 4D Radar-Grounded Semantic Reasoning and Trajectory Arbitration for Autonomous Driving](https://mlsanity.com/paper/2609.32681/) (2026)

## Abstract (arXiv)

Simultaneous Localization and Mapping (SLAM) is moving towards a robust perception age. However, LiDAR- and visual- SLAM may easily fail in adverse conditions (rain, snow, smoke and fog, etc.). In comparison, SLAM based on 4D Radar, thermal camera and IMU can work robustly. But only a few literature can be found. A major reason is the lack of related datasets, which seriously hinders the research. Even though some datasets are proposed based on 4D radar in past four years, they are mainly designed for object detection, rather than SLAM. Furthermore, they normally do not include thermal camera. Therefore, in this paper, NTU4DRadLM is presented to meet this requirement. The main characteristics are: 1) It is the only dataset that simultaneously includes all 6 sensors: 4D radar, thermal camera, IMU, 3D LiDAR, visual camera and RTK GPS. 2) Specifically designed for SLAM tasks, which provides fine-tuned ground truth odometry and intentionally formulated loop closures. 3) Considered both low-speed robot platform and fast-speed unmanned vehicle platform. 4) Covered structured, unstructured and semi-structured environments. 5) Considered both middle- and large- scale outdoor environments, i.e., the 6 trajectories range from 246m to 6.95km. 6) Comprehensively evaluated three types of SLAM algorithms. Totally, the dataset is around 17.6km, 85mins, 50GB and it will be accessible from this link: https://github.com/junzhang2016/NTU4DRadLM