---
title: 'M3DGR Dataset: Multi-Sensor SLAM Benchmark'
url: https://www.emergentmind.com/topics/m3dgr-dataset
type: topic
---

# M3DGR Dataset: Multi-Sensor SLAM Benchmark

Searching arXiv for recent papers on M3DGR to ground the article in the relevant literature.
M3DGR is a ground-robot SLAM benchmark introduced as a **“Multi-sensor, Multi-scenario and Massive-baseline SLAM dataset for Ground Robot”**. It was designed as a **sensor-rich benchmark with systematically induced degradation patterns including visual challenge, LiDAR degeneracy, wheel slippage and GNSS denial**, with the explicit goal of enabling standardized robustness analysis rather than only nominal-condition accuracy measurement. In the original benchmark paper, M3DGR is positioned as a response to the limited sensor coverage, limited configurability, and weak degradation modeling of prior datasets, and as a platform large enough to support evaluation of **40 SLAM systems** under controlled corner cases [2507.08364].

## 1. Origin and benchmark rationale

M3DGR was created to address two gaps identified in ground-robot SLAM benchmarking. The first is the **lack of standardized, configurable benchmarks** for systematically evaluating algorithms under degradations such as poor lighting, geometric degeneracy, wheel slip, and GNSS outage. The second is the **lack of rich multi-sensor coverage for robustness analysis**, since many earlier datasets fix the sensor setup or emphasize only one modality. The benchmark paper therefore frames M3DGR as a dataset for **systematic robustness testing**, **sensor-selection analysis**, **evaluation across diverse fusion configurations**, and **fair comparison of many SLAM systems under corner cases** [2507.08364].

The original paper explicitly contrasts M3DGR with earlier datasets such as **EuRoC, UrbanLoco, OpenLoris-Scene, M2DGR, Ground-Challenge, M2DGR-Plus, and MARS-LVIG**, arguing that existing resources often support only a narrow set of sensor types, do not provide configurable rich sensor suites, lack systematic degradation scenarios, or use pseudo-ground-truth from SLAM itself rather than external high-precision tracking. M3DGR is presented as overcoming those limitations through a **full sensor suite**, **controlled degradation scenarios**, and **accurate ground truth from external tracking** [2507.08364].

A central implication of this design is that M3DGR is not organized merely as a data repository. It is a benchmark whose sequence construction, ground-truth strategy, and degradation taxonomy are all tailored to failure analysis. The paper’s broader conclusion is that robustness depends not only on adding sensors, but on **adaptive sensor selection** and **degradation-aware switching**; M3DGR is intended to make those questions experimentally accessible [2507.08364].

## 2. Robotic platform and sensing stack

M3DGR is built on a **differential-drive ground robot** with **two motorized wheels**, which makes wheel odometry both naturally available and directly stressable in wheel-slip scenarios. The platform carries a multi-modal sensing stack consisting of **RGB-D camera + IMU**, **omnidirectional camera**, **two non-repetitive scanning LiDARs**, **wheel odometer**, and **two GNSS receivers / GNSS-related devices**, together with external tracking systems for ground truth [2507.08364].

The main onboard modalities are specified as follows: **Intel RealSense D435i RGB** at **640×480**, **69° horizontal × 42° vertical**, **30 Hz**; **RealSense D435i Depth** at **640×480**, **87° horizontal × 58° vertical**, **30 Hz**; **RealSense D435i IMU**, **6-axis**, **200 Hz**; **Insta360 X4**, **2880×1440**, **360°**, **15 Hz**; **Livox Avia**, **non-repetitive**, **450 m range**, **70.4° × 77.2°**, **15 Hz**, with **IMU: 200 Hz**; **Livox MID360**, **non-repetitive**, **40 m range**, **360° × 59°**, **15 Hz**, with **IMU: 200 Hz**; **wheel odometer**, **20 Hz**; **GNSS receiver**, **BDS/GPS/GLONASS/Galileo**, **10 Hz**; **RTK receiver**, **0.8 cm horizontal / 1.5 cm vertical localization accuracy**, **15 Hz**; and **Optitrack mocap**, **1 mm localization accuracy**, **360 Hz** [2507.08364].

The recording pipeline uses **ROS rosbag** with a **unified timestamp mechanism**. The paper also notes **hardware synchronization** for several sensors: the **RealSense D435i** internally synchronizes RGB, depth, and IMU, and both **Livox LiDARs** have built-in IMUs and use hardware triggering to ensure internal synchronization. Data acquisition and storage are handled by a high-performance **Intel NUC** with a **high-speed NVMe SSD** [2507.08364].

Later method papers underline why the dual-LiDAR configuration matters for algorithm analysis. In Surfel-LIO, M3DGR is described as a **“multi-sensor dataset designed for ground robot localization”** with **two Livox LiDAR sensors** having distinct characteristics: **AVIA** with a **non-repetitive scanning pattern** and about **70° field of view**, and **Mid-360** with **360° horizontal field of view** and higher point density. The same paper reports about **24,000 points/scan** for AVIA and about **15,000 points/scan** for Mid-360, which makes M3DGR useful for analyzing how **scan density** and **scanning pattern** affect runtime and state-estimation behavior [2512.03397].

## 3. Scenario taxonomy, induced degradations, and dataset scale

M3DGR covers both **indoor and outdoor** environments and is organized around four controlled degradation categories—**Visual challenge**, **LiDAR degeneracy**, **Wheel slippage**, and **GNSS denial**—plus **Standard** sequences. The benchmark table reports **32 sequences total**, **11,868.95 m** total distance, **14,821 s** total duration, and **225.0 GB** total data size [2507.08364].

| Category | Sequences | Distance / Duration / Size |
|---|---:|---|
| Visual challenge | 16 | 4,155.10 m / 5,565 s / 66.4 GB |
| LiDAR degeneracy | 3 | 1,016.28 m / 1,395 s / 23.1 GB |
| Wheel slippage | 7 | 1,008.69 m / 1,401 s / 26.3 GB |
| GNSS denial | 2 | 1,162.39 m / 1,359 s / 23.2 GB |
| Standard | 4 | 4,485.49 m / 5,101 s / 86.0 GB |
| **Total** | **32** | **11,868.95 m / 14,821 s / 225.0 GB** |

The **Visual challenge** category contains four subtypes: **Dark**, **Varying illumination**, **Dynamic scenes**, and **Occlusion**. Indoors, low light is induced by turning off room lights and using only a mobile phone flashlight; varying illumination is induced by alternating room lights at regular intervals; dynamic scenes are induced by a person moving in front of the robot; and occlusion is induced by deliberately blocking the camera. Outdoors, the corresponding cases include nighttime dark sequences, changing lighting, pedestrians and cyclists, moving vehicles, and partial camera obstruction. The summary table reports **5 dark sequences**, **4 varying-illumination sequences**, **3 dynamic sequences**, and **4 occlusion sequences**, for **16 sequences** in total [2507.08364].

The **LiDAR degeneracy** category contains **Corridor** and **Elevator transition** sequences. The corridor case is induced by driving through a feature-poor, geometrically repetitive corridor, and the elevator case by moving from a corridor into an elevator, creating a sudden change from structured planar geometry to a sparse vertical space. The benchmark reports **2 corridor sequences** and **1 elevator sequence**. In both cases, the robot is driven in a **loop**, returning to the starting position [2507.08364].

The **Wheel slippage** category contains **Wheel float**, **Sharp turn**, **Grass**, and **Rough road**. These are induced by lifting the driving wheels to force slip, commanding rapid directional changes at high speed, traversing uneven grassy terrain, and driving over roads with abrupt elevation changes. The summary table reports **2 wheel-float sequences**, **2 sharp-turn sequences**, **2 grass sequences**, and **1 rough-road sequence**, for **7 sequences** in total [2507.08364].

The **GNSS denial** category contains **2 GNSS-denial sequences**. In this setup, the robot starts in a GNSS-available area, traverses a GNSS-denied region, and then returns to the starting point. This structure is explicitly designed to expose the behavior of fusion systems when absolute positioning becomes unavailable and then reappears [2507.08364].

The paper also lists example sequence characteristics such as **Dynamic01: 175 s, 72.22 m**, **Corridor01: 403 s, 332.32 m**, **Elevator01: 699 s, 470.64 m**, and **GNSS-denial01: 609 s, 524.37 m**. This suggests that M3DGR is meant to span both short controlled stress tests and longer loop-like traversals in which accumulated drift becomes observable [2507.08364].

## 4. Ground truth, synchronization, and evaluation semantics

Ground truth in M3DGR is generated using external tracking rather than SLAM-derived pseudo-reference. The dataset uses **Optitrack mocap** for **indoor ground truth** and **RTK** for **outdoor ground truth**. For loop-based or special-case sequences—especially in **LiDAR degeneracy** and **GNSS denial**—the paper uses **ArUco marker detection** to compute the **relative pose between start and end**, which serves as a quantitative measure of trajectory accuracy when dense full-trajectory truth is harder to obtain in degraded settings [2507.08364].

The benchmark’s primary trajectory metric is **ATE RMSE**, used to compare estimated trajectories against ground truth. In the visual-challenge figure, if a system fails, the paper sets the error to **20 m** for plotting purposes. For degradation characterization, the paper also reports **visual average keypoints per frame**, **LiDAR average matching error**, **wheel drift rate**, and **GNSS average valid satellite count**. The reported values are **117 vs 138** keypoints per frame, **0.14 vs 0.04** LiDAR matching error, **10.24 m vs 2.04 m** wheel drift rate, and **4 vs 13** GNSS valid satellites; these values are used to argue that M3DGR is a harder and more realistic robustness benchmark [2507.08364].

Later studies adopt compatible but task-specific evaluation conventions on M3DGR. Surfel-LIO evaluates odometry using **APE (Absolute Pose Error) RMSE** of translational differences after **SE(3) alignment**, computed with the **evo** toolkit, under controlled settings including identical **0.5 m** voxel resolution and identical **200 m × 200 m** local map extent. FAST-LIVGO uses **ATE RMSE (m)** across open-sky and GNSS-denied sequences, again relying on M3DGR’s externally referenced trajectories [2512.03397][2606.19190].

A plausible implication is that M3DGR’s ground-truth design serves two distinct purposes. First, it supports conventional full-trajectory benchmarking where external truth is available. Second, in scenarios deliberately constructed around return-to-start loops or GNSS-degraded regions, it supports relative-drift evaluation that is tightly matched to the underlying failure mode being studied.

## 5. Benchmark use in SLAM, LIO, and sensor-fusion research

The original M3DGR paper evaluates **40 SLAM systems**, spanning **vision-based methods**, **LiDAR-based methods**, **LiDAR-visual fusion systems**, **wheel odometry**, and **GNSS SPP**. The main findings are organized by degradation mode. Under **Visual challenge**, performance degrades steadily as the environment becomes darker, more occluded, or more visually dynamic, and pure vision systems are highly vulnerable to feature loss. Under **LiDAR degeneracy**, corridor and elevator sequences cause severe drift or outright failure in many LiDAR methods, while **continuous-time LiDAR methods** perform better than discrete-time methods in general; **CT-LIO** is highlighted as particularly robust in elevator transition, and **VoxelMap**, **HM-LIO**, and **Fast-LIVO2** are also reported as more stable than many others. Under **Wheel slippage**, wheel-integrated systems often drift badly under slip. Under **GNSS denial**, GNSS-integrated methods degrade substantially without absolute positioning, while adding wheel odometry helps reduce drift [2507.08364].

The same paper uses M3DGR to motivate **Ground-Fusion++**, a modular framework that couples **GNSS**, **RGB-D**, **IMU**, **wheel odometry**, and **LiDAR** with adaptive switching under degradation. On **Longtime01** it reports **ATE RMSE of 7.5 m**, compared to **22.5 m** for **Ground-Fusion**, and for LiDAR degradation it reports **1.67 m / 1.73 m** on two corridor sequences for the proposed system, compared with **6.14 m / 26.66 m** for the **LIO subsystem alone**. These results are method-specific, but they also show how the dataset is used to study failure-aware fusion rather than only single-modality accuracy [2507.08364].

In Surfel-LIO, M3DGR is the **sole experimental benchmark**. The paper evaluates on **nine M3DGR sequences**—**Dark01–02**, **Dynamic03–04**, **Occlusion03–04**, and **Varying-illu03–05**—chosen because they stress low-light indoor conditions, dynamic objects, partial sensor blockage, and rapid lighting changes. The method compares against **Fast-LIO2** and **Faster-LIO** and uses M3DGR to support two claims: that **Surfel-LIO is much faster than Fast-LIO2 and Faster-LIO**, especially on dense scans, and that it preserves odometry accuracy while improving speed. The authors also emphasize that M3DGR’s dual-sensor configuration lets them analyze how dense AVIA scans and lower-density Mid-360 scans change computational load and estimation quality [2512.03397].

In FAST-LIVGO, M3DGR is the main public benchmark for a **LiDAR-Inertial-Visual-GNSS** fusion system. The paper uses **Varying-illu03**, **Varying-illu04**, **Varying-illu05**, **Outdoor01**, **Outdoor04**, **Dark01**, and **Dark02** to separate **open-sky scenarios** from **GNSS-denied scenarios**. It argues that M3DGR is well suited because it provides **RTK ground truth** and contains conditions that stress both **LIVO degeneracy** and **GNSS degradation**. The reported result is that the full method achieves the lowest **ATE RMSE** on every reported sequence, while the **Ours w/o Rejection** ablation fails completely in **Dark01** and **Dark02**, which the paper uses as evidence that tightly coupled fusion is brittle without robust rejection logic [2606.19190].

Taken together, these later papers show that M3DGR has become more than a one-off benchmark. It is used to evaluate runtime scaling across LiDAR scan patterns, robustness under dark and dynamic conditions, behavior under GNSS corruption or denial, and the interaction between local odometry quality and global consistency.

## 6. Nomenclature and relation to similarly named datasets

M3DGR should be distinguished from several similarly named datasets. It is not **M2DGR**. The 2021 paper **“M2DGR: A Multi-sensor and Multi-scenario SLAM Dataset for Ground Robots”** consistently refers to **M2DGR**, states that the paper is **about M2DGR, not M3DGR**, and explicitly notes that it **never introduces a dataset named M3DGR** and does not describe any formal relation or renaming from M2DGR to M3DGR [2112.13659]. M3DGR is therefore a separate benchmark rather than a renamed edition of M2DGR.

It is also unrelated to **M³D-Stereo**, the stereo image restoration dataset introduced in **“M3D-Stereo: A Multiple-Medium and Multiple-Degradation Dataset for Stereo Image Restoration”**, which contains **7904 high-resolution stereo image pairs** for adverse-condition restoration research in underwater, haze/fog, and low-light settings [2604.12917]. Despite the lexical similarity, that dataset addresses image restoration and stereo matching rather than ground-robot SLAM.

Likewise, M3DGR is unrelated to **M\(^3\)D**, the multimodal information-extraction dataset introduced in **“M$^{3}$D: A Multimodal, Multilingual and Multitask Dataset for Grounded Document-level Information Extraction”**, which contains **4,093 documents/videos** and supports **Named Entity Recognition**, **Entity Chain Extraction**, **Relation Extraction**, and **Visual Grounding** [2412.04026].

Within robotics, the distinctive feature of M3DGR is the combination of a **full sensor suite**, **systematically induced degradation scenarios**, **externally referenced ground truth**, and a benchmark protocol expressly organized around failure modes. This suggests that its main contribution is not only additional data volume, but a reproducible experimental structure for studying how and why ground-robot SLAM systems break under adverse sensing conditions.

Source: https://www.emergentmind.com/topics/m3dgr-dataset