Get your off-road data labeled fast and accurately
For off-road mobile robots, efficiency is deeply tied to the quality of data labeling. Higher quality annotation leads to more autonomy and precise navigation, especially across intricate terrains. This extends from detecting larger, static objects to smaller, dynamic ones like trash, branches, and personal items.
Ensure accurate object tracking even amidst the toughest terrains. Whether it’s an agriculture tractor navigating through a field or a construction machine at a dynamic site, you get consistent tracking IDs across all sensors and sequences.
Unified object identification: A robot fitted with a lidar and multiple cameras can consistently identify an object across different views. A boulder or a tree labeled in 3D will maintain its ID in 2D image sequences, streamlining your labeling process and improving your ML accuracy.
Enhance efficiency: Cut down on reconciliation time, ramp up model precision, and get a reliable track of objects over various frames and sensors.
Manage occlusions: Seamlessly split existing tracks or reconnect to occluded objects, ensuring your robot recognizes obstacles even when temporarily out of sight.
Get your data labeled faster, even for complex off-road scenarios. You can project 3D data to 2D effortlessly, allowing rapid and consistent labeling.
Simplified labeling: Say goodbye to manual, tedious tasks. One-click projects point cloud data to your camera sensor data, providing pre-labeled datasets for minimal corrections.
Versatile export options: Cater to diverse robotic systems by exporting labeled data in various formats.
Gain deeper, more accurate insights for your off-road robotics by combining data from multiple sensors. Understand your environment better, distinguishing between critical obstacles like rocks and shrubs.
Richer context for labelers: Overlaying 2D and 3D sensor data provides an enriched view. This means distinguishing between a rock and a cardboard box becomes easier, leading to precise labeling.
Cost & time efficiency: By letting a single expert label data from all sensors, you maintain consistency and reduce overheads, making the most of your resources.
Swift object recognition: Combine camera images with 3D point clouds for faster and more accurate object identification, ensuring your mobile robots navigate the toughest terrains with ease.
Label all your sensor’s data in one interface
Label fast, accurate and efficiently with ML-assisted features build for and by machine learning teams.
