Our solutions are truly generic by nature.


Modern working machines are slowly turning into mobile robots

Future autonomous or semi-autonomous mobile working machines will be capable of navigating in an unstructured and partially dynamic environment and simultaneously work towards given mission objectives without being extensively controlled by human operators.
To do that an autonomous field and service robot must not only localise itself (PAIKKA) and map the environment (KARTTA), but also sense, perceive, classify and model what is currently happening around it and even predict what will most likely happen in the near future (TARKKA).  In addition, the machine should be connected to any available supportive infrastructure (e.g., localization services, dynamic maps, etc.) and have tight interactions with other autonomous mobile machines sharing the same operation space.


The traditional machine chain in Nordic Cut_To_Length (CTL) forest harvesting system consists of a one-grip-harvester for felling, debranching and cutting the stems to logs, and a forwarder for transporting the logs from forest to roadside in separate heaps for each sorts of timber. Forest trucks transport the timber to sawmills and factories. The productivity of a machine chain is currently greatly defined by the experience of the operators of the forestry machines. Therefore, there are substantial economic advantages when the level of autonomy is increased, and assistive operations are successfully implemented.


Current autonomous mobile robotic solutions can already perform several classical agricultural tasks, such as pruning, planting, harvesting, monitoring and others. In most of the open field agricultural applications, the localisation is based on Global Navigation Satellite System (GNSS), but in some environments, satellite signals just cannot be trusted. Those situations are the ones where our feature-based localisation product excels.

Environmental maintenance

This domain is extensive and includes all sorts of maintenance tasks conducted with autonomous robots or advanced mobile working machines. We have been cleaning streets and parking halls with Trombia Technologies. The machine must be able to localise itself in all conditions, create a detailed 3D environmental model and have a real-time situational awareness working in all weather conditions.


The ship-based system should recognize static and dynamic obstacles around the ship, predict their motion  and plan a safe and efficient trajectory for the ship. We are experts in a) fusing thermal and color camera data to enable visual vessel detection and tracking in all lighting conditions, b) fusing lidar, camera and radar data to accurately segment, classify and track any objects of interest, c) comparing sea chart and 3D sensor data to automatically extract all moving targets on the surface and d) calibrating multiple sensors into a common coordinate frame, enabling advanced sensor fusion.  GNSS should not be your only option for the localisation task. Man-made urban harbours and inland waterways have usually plenty of topological landmarks, which will be used as features to create the topological map. This is something we have done in our previous projects worldwide.


Autonomous railway-based systems have been existing already for some time, especially in closed environments, which have been controlled by external infrastructure-based solutions. In a complex dynamic urban environment, the requirements are much tighter. Usually, cameras, LiDARs and radars are included.  In urban settings, there will always be areas where the use of traditional GNSS based localization is not possible. This localization system is usually based on a constantly updated feature-based map, sensory system capable of detecting those selected features in all conditions, and the algorithms for the real-time 6D pose estimates. 3D localization and environmental modeling provide the foundations for obstacle detection and the following collision warning signals for the driver.


Skoda ACS
Trombia Technologies