Why MOLA
MOLA is a production-grade LiDAR SLAM framework for robotics, autonomous vehicles, and 3D surveying. It follows an Open Core model: the full framework is open-source and free for research, while commercial users can upgrade to MOLA Pro for closed-source deployment, pre-built binaries, and priority support.
What makes MOLA different
Fully configurable - no recompilation
MOLA pipelines are defined entirely in YAML. Swap ICP matchers, change map layers, adjust filter chains, or switch between LO and LIO - all without rebuilding a single line of C++. No other SLAM framework offers this level of runtime configurability. This means faster iteration during development and safer updates in production.
Map-less RTK-quality outdoor georeferencing
Using the smoother state estimator with a low-cost GNSS receiver + LiDAR + IMU, MOLA delivers geodetic-quality pose estimation in UTM/ENU coordinates - without a prebuilt map and without an RTK base station. This is ideal for outdoor robots, agricultural vehicles, and autonomous driving applications where absolute positioning matters.
Rich metric map ecosystem
MOLA’s .mm metric map format comes with a full set of CLI tools:
mm-viewer: Interactive 3D map viewer
mm2las, mm2ply, mm2txt: Export to LAS, PLY, TXT for GIS/surveying workflows
mm-filter: Filter and transform maps
mm-georef: Georeference maps
mm-info: Inspect map metadata
This makes MOLA maps interoperable with industry-standard tools like CloudCompare, QGIS, and surveying software.
ROS 2 native AND standalone C++
Deploy with full ROS 2 integration (Humble, Jazzy, Kilted, Rolling) or as a standalone C++ application without any ROS dependency. No other SLAM SDK offers both cleanly. This gives you maximum flexibility for edge deployment, Docker containers, or integration into proprietary frameworks.
Multi-sensor, multi-environment
LiDAR-only odometry (LO) - no IMU required
LiDAR-inertial odometry (LIO) - with IMU fusion
GNSS fusion - consumer-grade GPS for georeferencing
Kinematics fusion - wheel encoders, vehicle odometry
Validated across urban driving (KITTI), agricultural environments (GreenBot), indoor (warehouses, buildings), outdoor (forests, campuses), and aerial (drones).
Academic rigor, production ready
MOLA is backed by peer-reviewed publications in top venues:
IJRR 2025: A flexible framework for accurate LiDAR odometry, map manipulation, and localization
RSS 2019: A Modular Optimization Framework for Localization and Mapping
Benchmarked on KITTI (0.4-2.0% translation error), MulRan, HILTI, Kaist, and custom datasets. This is not a paper prototype - it is actively deployed in real-world robots.
Pre-built for amd64 and arm64
Binary packages are available from the ROS build farms for both amd64 and arm64 (Jetson, Raspberry Pi). MOLA Pro subscribers additionally get access to a private apt repository and Docker images for streamlined deployment.
Use cases
MOLA is used across a wide range of industries and environments: