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.