Model Categories
Our AI models are organized into the following categories, each addressing specific robotics challenges:Depth Estimation
Models for perceiving depth from images, crucial for 3D scene understanding and obstacle avoidance.
Object Detection
Identify and locate objects within an image or video stream.
Feature Matching
Models for finding corresponding points between images, essential for tasks like SLAM and image stitching.
Navigation
AI models to enable autonomous movement and path planning.
Optical Flow
Estimate the motion of objects or the camera itself between consecutive frames.
Segmentation
Partition an image into multiple segments or regions, often to distinguish objects from the background.
Simultaneous Localization and Mapping (SLAM)
Enable a robot to build a map of an unknown environment while simultaneously keeping track of its location within that map.
Tracking
Follow the movement of specific objects over time in a video sequence.
Time to Collision (TTC)
Estimate the time remaining before a potential collision, critical for safety systems.
Vision Language Models (VLA)
Models that can understand and respond to queries about visual content using natural language.
Visual Language Models (VLM)
Integrate visual information with natural language processing for enhanced understanding and interaction.
Visual Odometry (VO)
Estimate the camera’s motion by analyzing sequential camera images.