Sign in

Inna Nomerovska

Inna Nomerovska

Inna Nomerovska is the VP of Marketing and Brand Strategy at Keymakr | Keylabs. She is a tireless technology enthusiast with 15 years of experience in international marketing and startup background.

Validating Simulated Sensor Data Against Real-World Annotations for Autonomous Driving

Validating Simulated Sensor Data Against Real-World Annotations for Autonomous Driving

The rapid development of autonomous vehicle technologies is driving a growing need for large volumes of high-quality data to train, test, and validate computer vision algorithms and environmental perception systems. Modern driverless cars use a suite of sensors, including cameras, LiDARs (LiDAR), radars, and other sensors, which provide information

3D Occupancy Grid Annotation for Autonomous AI

3D Occupancy Grid Annotation for Autonomous AI

3D occupancy grid annotation divides a 3D scene into cubic voxels (volumetric pixels) and classifies each of them as occupied, free, or unknown. This approach is used for training unmanned vehicles because it allows modeling arbitrary geometric shapes and objects that are difficult to describe with standard 3D bounding boxes.

A Comparative Study of Manual and Automated Annotation in Automotive

A Comparative Study of Manual and Automated Annotation in Automotive

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.

Abstract of LiDAR sensor degradation

Abstract of LiDAR sensor degradation

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

Training ADAS with Automotive Radar Data

Training ADAS with Automotive Radar Data

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

Annotating Data for Robot Safety Certification: ISO 10218 and Collaborative Robot Standards

Annotating Data for Robot Safety Certification: ISO 10218 and Collaborative Robot Standards

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,

Annotating deformed object manipulation

Annotating deformed object manipulation

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