🤖 Check out a video teaser for one of our four available datasets, specifically the 3D LiDAR Degenerate: Fields track called shellby-0225-test-loop.
A continuous, server-side evaluation platform designed to test the resilience of modern SLAM systems against real-world, perceptually-degraded data. This benchmark is part of the ECMR 2025 Workshop on "SLAM in Challenging Environments".
View All CompetitionsGo beyond clean, structured environments. We provide a benchmark with multi-modal datasets (degenerate LiDAR, forest Radar, multi-spectral) designed to break conventional algorithms and foster innovation.
Move beyond trajectory submissions. Our platform evaluates your entire Dockerized SLAM system on our private test servers, providing a fair assessment of your algorithm's true performance.
This is a rallying point for researchers focused on localization under ambiguity. Join a community dedicated to creating the next generation of truly challenge-aware navigation systems.
The August 28, 2025 deadline is for the competition associated with the workshop. However, the platform is a living benchmark and will remain open for submissions indefinitely, allowing you to track progress against new datasets and methods over time.
Participation is free and open to everyone. You do not need to register for the ECMR conference. All workshop competition participants will be offered an opportunity to present their system to the community online.
Submissions are ranked based on standard metrics, including Absolute Trajectory Error (ATE) and Relative Pose Error (RPE).
The following competitions are currently open for submission. Each contains unique datasets for training, while the private evaluation data includes larger loops and parts not seen in the public training sets.
🤖 Check out a video teaser for one of our four available datasets, specifically the 3D LiDAR Degenerate: Fields track called shellby-0225-test-loop.
This track, recorded in a forested area at Örebro University, captures the challenging Enbuskabacken "Viking Hill" terrain during peak summer vegetation. The uneven ground, which served as a Viking Age burial site, provides a unique test for SLAM algorithms.
Recorded in June, the grass was tall enough to often obscure the robot sensors.
The robot was intentionally driven through bushes and over uneven terrain.
Click on the links below to watch video previews of the Viking Hill dataset:
Have a dataset that pushes the boundaries of SLAM? We invite researchers to contribute their own challenging scenarios. Help grow the community's resources and see how other systems perform on your data.
View Contributor GuidelinesThe organization and infrastructure support of the SLAM competition would not be possible without contributing CRL Lab members at the Czech Technical University in Prague. A huge thanks is going to Vsevolod Hulchuk, Rudolf Jakub Szadkowski, Jindřiška Deckerová, Jan Bayer, Martin Škarytka, and Tomáš Lapeš. We also extend our sincere gratitude for the dataset contributions from Vladimír Kubelka (Örebro University, Sweden) for the Radar Dataset, and from Matteo Luperto (University of Milano, Italy) and Dyuman Aditya (Ecole Centrale de Nantes, France) for the Quadruped Dataset. This work was supported by the EU funded project ROBOPROX - Robotics and advanced industrial production (reg. no. CZ.02.01.01/00/22_008/0004590). The work on the Radar Dataset was supported by the European Union's Horizon Europe Framework Programme under the RaCOON project (ID: 101106906).