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Creating a reliable reference dataset (cross-sensor ground truth) is a challenge for autonomous vehicle (AV) developers. Each sensor sees the world in its own way: cameras provide semantics but are weak at determining distance; LiDAR provides 3D geometry but cannot see through heavy rain or fog; Radar measures speed

The rapid development of AI, computer vision, and machine learning has had a significant impact on the automotive industry. Modern driver assistance systems (ADAS) and autonomous vehicles use algorithms that require large amounts of well-labeled data for training and evaluation. Traditionally, image and video annotation has been performed manually.

Currently, automotive autonomous and semi-autonomous driving systems rely on a complex suite of sensors that includes optical cameras, radars, and high-precision LiDARs. Despite their significant operating range and ability to build detailed three-dimensional maps of space, these technologies prove vulnerable in the immediate vicinity of the vehicle&

LiDAR has become an important perception sensor in autonomous vehicles, robotics, industrial automation, and smart infrastructure. By creating highly accurate 3D point clouds, LiDAR enables AI systems to detect obstacles, estimate distances, localize objects, and generate detailed environmental models. However, environmental conditions, aging equipment, contamination, and mechanical failures can degrade

Camera calibration is one of the fundamental stages in building modern computer vision systems, as it ensures an accurate geometric correspondence between the scene's three-dimensional space and its two-dimensional image. Determining the camera's internal and external parameters, as well as compensating for optical distortions,

The development of modern advanced driver-assistance systems (ADAS) and autonomous transport directly depends on the vehicle's ability to continuously and flawlessly recognize its surrounding environment. Currently, most autopilot architectures rely on a synergy of optical cameras and LiDAR, which provide high resolution and precise geometry under ideal

Driver assistance systems (ADAS) combine information from cameras, lidar, radar, GPS, inertial module, and other sensors to create a comprehensive view of the road. This process, known as sensor fusion, improves the accuracy of perception, object detection, and decision-making in complex environments. However, effective sensor fusion requires precisely synchronized

The rapid development of industrial automation and the widespread adoption of collaborative robots (cobots) are significantly transforming modern manufacturing processes. This opens up new opportunities to increase productivity, production flexibility, and operational efficiency. In modern robotic solutions, data annotation is becoming increasingly important, especially for systems that use computer vision,

Modern automation of logistical and industrial facilities requires a radical change in artificial intelligence architecture: a transition from egocentric behavioral models to collective interaction systems. Traditional training datasets instill a purely individual logic of actions in a robot, where the machine relies exclusively on its own sensors, independently calculates its

Robotics can manipulate rigid objects, but deformed objects remain a challenge in embodied AI. Objects such as clothing, cables, ropes, bags, and soft materials change shape during interaction. Their behavior depends on gravity, contact forces, friction, and previous states, making perception and manipulation more complex. Robot foundation models require specialized

The rapid advancement of AI and robotic technologies has significantly transformed modern healthcare, particularly in surgery. AI-assisted surgical robotics systems are increasingly being integrated into clinical practice to enhance procedural accuracy, support decision-making, and improve patient outcomes. However, the effectiveness and reliability of these intelligent systems depend heavily

Warehouse premises are among the most complex environments for artificial intelligence systems, where standard computer vision algorithms trained on classic open datasets regularly fail. The main challenge here is the organization of the assortment, where thousands of visually identical boxes differing only by a serial number on a tiny barcode

Modern robotics relies on simulation environments to accelerate AI development. Rather than relying on physical robots operating in real-world environments, organizations are generating large amounts of synthetic training data in virtual worlds. Simulation platforms allow for experimentation, testing, and large-scale scenario generation. However, a challenge in robotics remains