# SNAIL Radar: A large-scale diverse benchmark for evaluating 4D-radar-based SLAM

arXiv: https://arxiv.org/abs/2407.11705 · HTML: https://mlsanity.com/paper/2407.11705/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Jul 2024.

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

- [4D mmWave Radar for Sensing Enhancement in Adverse Environments: Advances and Challenges](https://mlsanity.com/paper/2503.24091/) (2025)
- [DNOI-4DRO: Deep 4D Radar Odometry with Differentiable Neural-Optimization Iterations](https://mlsanity.com/paper/2505.12310/) (2025)
- [RLPR: Radar-to-LiDAR Place Recognition via Two-Stage Asymmetric Cross-Modal Alignment for Autonomous Driving](https://mlsanity.com/paper/2603.07920/) (2026)
- [G-EDF-Loc: 3D Continuous Gaussian Distance Field for Robust Gradient-Based 6DoF Localization](https://mlsanity.com/paper/2604.04525/) (2026)
- [RCVLA: 4D Radar-Grounded Semantic Reasoning and Trajectory Arbitration for Autonomous Driving](https://mlsanity.com/paper/2609.32681/) (2026)

## Abstract (arXiv)

4D radars are increasingly favored for odometry and mapping of autonomous systems due to their robustness in harsh weather and dynamic environments. Existing datasets, however, often cover limited areas and are typically captured using a single platform. To address this gap, we present a diverse large-scale dataset specifically designed for 4D radar-based localization and mapping. This dataset was gathered using three different platforms: a handheld device, an e-bike, and an SUV, under a variety of environmental conditions, including clear days, nighttime, and heavy rain. The data collection occurred from September 2023 to February 2024, encompassing diverse settings such as roads in a vegetated campus and tunnels on highways. Each route was traversed multiple times to facilitate place recognition evaluations. The sensor suite included a 3D lidar, 4D radars, stereo cameras, consumer-grade IMUs, and a GNSS/INS system. Sensor data packets were synchronized to GNSS time using a two-step process including a convex-hull-based smoothing and a correlation-based correction. The reference motion for the platforms was generated by registering lidar scans to a terrestrial laser scanner (TLS) point cloud map by a lidar inertial sequential localizer which supports forward and backward processing. The backward pass enables detailed quantitative and qualitative assessments of reference motion accuracy. To demonstrate the dataset's utility, we evaluated several state-of-the-art radar-based odometry and place recognition methods, indicating existing challenges in radar-based SLAM.