SLAM Datasets for Simultaneous Localization and Mapping
Multi-sensor SLAM sequences with ground truth trajectories, loop closure events, and challenging condition coverage. Built for developing and evaluating production localization and mapping systems.
20+
Years in AI training data
1,000+
Language locales
1M+
Hours of video annotated
ISO
27001 certified
The Challenge
Beyond Standard SLAM Benchmarks
KITTI Odometry and TUM RGB-D established SLAM evaluation standards. Production localization and mapping systems require data from your specific sensor configuration, environment type, and operational conditions those benchmarks do not cover.
Mobile robot SLAM in warehouses, drone navigation in construction sites, and wearable AR localization in indoor spaces each require sensor-matched sequences with ground truth trajectories. Generic SLAM benchmarks use different sensors and environments.
Off-the-shelf SLAM datasets suffer from sensor mismatch (benchmark sensors differ from your production hardware) and environment gaps (outdoor urban driving and lab corridors do not represent your deployment scenario).
LXT builds custom SLAM datasets with your sensor configuration, environment types, and motion profiles. We deliver multi-sensor sequences with accurate ground truth trajectories for SLAM algorithm development and evaluation.
Why Teams Upgrade
Limitations of Public SLAM AI Datasets
Standard benchmarks serve research well. Production deployments need more.
| Dataset | Primary Limitation | Impact |
|---|---|---|
| KITTI Odometry | Outdoor urban driving only; specific Velodyne LiDAR and stereo camera rig; no indoor, handheld, or drone perspectives | Outdoor driving |
| TUM RGB-D | Handheld RGB-D only; limited scene diversity; specific Kinect sensor; no LiDAR or outdoor coverage | RGB-D only |
| EuRoC MAV | Micro air vehicle indoor only; specific IMU and stereo rig; no outdoor, ground robot, or wearable motion profiles | MAV indoor |
| Hilti SLAM | Construction environments but specific sensor platform; limited to industrial outdoor; annual challenge scope | Competition scope |
| Replica | Photorealistic synthetic environments only; no real sensor noise; sim-to-real gap limits production applicability | Synthetic only |
Not sure which specs you need?
Our data specialists help you scope the right dataset for your model architecture.
Configurable Specifications
Specs Built Around Your Model
Public datasets come fixed. Yours is configured for your architecture, environment, and use case.
Sensor Suite
Hardware Configuration
- Cameras: Monocular, stereo, fisheye, and event cameras per your platform
- LiDAR: 2D and 3D LiDAR with your beam count and scan rate
- IMU and GNSS: High-rate IMU and GPS/RTK for trajectory ground truth reference
Environment Types
Scene Coverage
- Indoor: Warehouses, factories, offices, and featureless corridors
- Outdoor: Urban, suburban, forest, construction, and GPS-denied outdoor
- Mixed: Indoor-outdoor transitions with dynamic lighting changes
Ground Truth
Trajectory Accuracy
- Motion Capture: Sub-millimeter accuracy for indoor trajectory ground truth
- RTK GNSS: Centimeter-level outdoor trajectory with RTK correction
- Loop Events: Annotated loop closure events for evaluating loop detection
Need a custom configuration?
We've built datasets across dozens of domains and use cases. Let's scope yours.
Capturing Complexity
Edge Cases in SLAM Datasets
High-accuracy models handle rare attributes that public datasets miss.
Featureless and Textureless Environments
Painted walls, open fields, and monotone surfaces challenge visual SLAM. Specifically collected feature-sparse sequences test odometry robustness in low-texture deployment environments.
Dynamic Object Contamination
Moving people, vehicles, and objects violate the static world assumption of most SLAM systems. Dynamic object annotations allow evaluation of dynamic-object-robust SLAM variants.
Aggressive and High-Speed Motion
Fast-turning or vibrating platforms produce motion blur and IMU saturation. High-speed sequences with matched ground truth test motion blur robustness.
Seasonal and Lighting Change
Long-term localization requires place recognition across appearance changes. Repeated trajectory collection across seasons, times of day, and weather provides appearance variation evaluation sequences.
Ground Truth Quality
Human-in-the-Loop Annotation
Precise annotation bridges raw data and learnable signal. Expert annotators deliver precision automated tools can't match.
Ground Truth Trajectory
Sub-centimeter accurate ground truth using motion capture or RTK GNSS. Trajectory interpolated to sensor timestamps and delivered in TUM, KITTI, and ROS bag formats.
Environment Metadata
Room and zone labels, dynamic object annotations, loop closure event markers, and lighting condition logs included per sequence.
Calibration Files
Full sensor calibration including intrinsics, extrinsics, IMU noise parameters, and time offset estimates delivered with each sequence set.
Industry Applications
SLAM Datasets for Your Domain
Custom taxonomies and collection protocols for specific deployment contexts.
Mobile Robotics
Warehouse, factory, and service robot navigation
Drone AI
Aerial localization, inspection, GPS-denied flight
AR and XR
Indoor spatial understanding, persistent AR anchoring
Construction AI
Site progress mapping, as-built verification
Autonomous Driving
HD map building, localization in dynamic environments
Asset Mapping
Indoor mapping, facility management AI
Outdoor Robotics
Terrain navigation, exploration robots
Research
SLAM algorithm development and evaluation
Compliance & Ethics
Secure and Ethical Data Collection
Data collection involving people and sensitive content requires robust security, compliance, and ethical protocols at every stage.
Global Demographic Reach
Collection across 1,000+ locales and diverse demographics to prevent algorithmic bias in your deployed models.
ISO 27001 Certified
Sensitive projects processed in certified secure facilities meeting the highest information security standards.
GDPR & Privacy Compliance
All collection and annotation protocols vetted for consent and privacy. Legally robust for global deployment.
Frequently Asked Questions
SLAM Dataset FAQs
Can you collect with our specific sensor platform?+
Yes. We configure collection rigs for your exact sensor combination. Extrinsic calibration between sensors is performed and delivered with each collection.
What ground truth accuracy do you achieve?+
Indoor motion capture provides sub-millimeter trajectory accuracy. Outdoor RTK GNSS provides 1-3 cm accuracy. Ground truth quality reports with covariance estimates are included.
Can you create sequences with specific challenges?+
Yes. We design sequences targeting specific SLAM challenges including dark environments, feature-sparse areas, aggressive motion, and loop closure scenarios per your evaluation requirements.
What delivery formats do you support?+
ROS bags, KITTI bin, TUM txt, and EuRoC CSV trajectory formats. Camera images as PNG or JPEG. LiDAR scans in PCD and bin format with calibration JSON.
Can you collect repeated sequences for long-term localization?+
Yes. We collect the same trajectory across multiple sessions at different times and conditions for long-term place recognition and appearance-change evaluation research.
What does a custom SLAM dataset cost?+
Projects range from $20K for focused single-environment sequence sets to $150K+ for large multi-environment, multi-sensor collections with motion capture ground truth.
How do you handle dynamic objects in ground truth generation?+
Dynamic objects are annotated separately from static structure. Separate static-only maps and dynamic object tracks are provided where evaluation requires clean static ground truth.
Get Started
Scope Your Custom SLAM Dataset
Share your sensor platform, environment type, and evaluation requirements. A robotics AI data specialist will provide a detailed proposal within 48 hours.
Please provide us with the details of your inquiry and one of our team members will be in touch.
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