Solutions

MOLA provides a modular set of solutions for LiDAR-based localization, mapping, and SLAM. All solutions are fully configurable via YAML - no recompilation needed.

See Plans and pricing for the comparison between Community (open-source) and Pro (commercial) editions.


1. LiDAR odometry and localization (LO / LIO)

LiDAR odometry is one of the most advanced and flexible LiDAR odometry modules available. It supports:

  • LiDAR-only odometry (LO) - no IMU required

  • LiDAR-inertial odometry (LIO) - fusing LiDAR with IMU for improved robustness

Both modes work in mapping (build a map from scratch) and localization (localize within a prebuilt map) configurations.

Key strengths: Sub-centimeter accuracy when tuned for survey-grade applications, or lightweight real-time performance for navigation. Configurable pipeline via YAML.

Get started: Build your first map | Mapping and localization tutorial | Ouster LIO tutorial

https://mrpt.github.io/imgs/mola-slam-kitti-demo.gif

2. Fused localization: LO/LIO + GNSS + kinematics

Based on the smoother state estimator, this solution improves localization in a prebuilt map by fusing:

  • LiDAR odometry output (LO or LIO)

  • GNSS (consumer-grade GPS receivers)

  • Kinematics (wheel encoders, vehicle odometry)

  • IMU data

This multi-sensor fusion provides robust, drift-corrected localization suitable for autonomous navigation in both indoor and outdoor environments. The smoother handles sensor outages gracefully - if GNSS signal is lost indoors, LiDAR odometry continues seamlessly.

Best for: Autonomous mobile robots (AMR), outdoor vehicles transitioning between GPS-available and GPS-denied areas, mixed indoor-outdoor scenarios.


3. Map-less georeferenced localization

Based on the smoother state estimator, this solution provides RTK-quality georeferenced pose estimation without a prebuilt map.

By fusing low-cost GNSS + LiDAR + IMU + kinematics, the smoother estimates the vehicle pose in geodetic (latitude/longitude) or UTM coordinates in real time. No RTK base station is required - a standard consumer-grade GNSS receiver is sufficient.

This enables:

  • Outdoor robot navigation with absolute positioning from the first second

  • Georeferenced trajectory logging for fleet management

  • Autonomous driving in open environments without prior mapping

Best for: Agricultural robots, autonomous tractors, outdoor inspection platforms, delivery robots, and any application requiring absolute outdoor positioning without the cost and complexity of RTK infrastructure.


4. Full 3D SLAM (georeferencing + loop closure)

Build globally consistent, georeferenced 3D maps, even in large-scale environments mixing indoor and outdoor areas. This is the most complete SLAM solution in MOLA, combining:

  • Georeferencing metric maps with consumer-grade GNSS sensors (see Georeferencing)

  • Offline loop closure for globally consistent maps - corrects accumulated drift over long trajectories

  • Simple-map → metric-map pipelines for flexible post-processing (see sm2mm pipelines)

https://mrpt.github.io/imgs/kaist01_georef_sample.png

Best for: Large-scale surveying, building 3D maps of campuses/cities, creating reference maps for localization.

Coming soon:

  • mola_3d_mapper: Full live/offline 3D SLAM with online loop closure (in development)

  • mola_2d_mapper: 2D SLAM via pose graph optimization for 2D LiDARs (in development)


Industry applications

Autonomous Mobile Robots (AMR)

Warehouse logistics, cleaning robots, inspection platforms. Real-time LO/LIO for navigation and prebuilt-map localization for autonomous operation.

Agriculture & Greenhouses

Autonomous tractors, greenhouse navigation, crop monitoring, and precision agriculture. Validated on the GreenBot dataset in Mediterranean greenhouse environments.

Automotive & ADAS

Urban autonomous driving and HD map generation. Benchmarked on KITTI with 0.6% translation error. Compatible with standard automotive sensor suites (LiDAR + IMU + GNSS).

Surveying & 3D Scanning

Backpack mapping, drone-based surveying, forest inventory. Export to LAS/PLY for GIS workflows. Sub-centimeter accuracy with survey-grade pipeline tuning.

Inspection

Industrial facility inspection, infrastructure monitoring, underground and mining environments. Works with handheld, backpack, and drone-mounted LiDARs.

Indoor Mapping

Building interiors, warehouses, offices. Create detailed 3D maps for facility management, renovation planning, or robot navigation.


Demos and videos

Forest inventory 3D mapping
Backpack 3D mapping indoors
Drone mapping - HILTI 2021
Greenhouse mapping

More demos on the MOLA YouTube playlist →