Perception & Machine Learning

Advancing Autonomy for Off-Road Scenarios

Perception in Challenging Environments

Neya focuses on the last 10% of the perception problem- bad weather and poor visibility. Combining a broad range of camera, stereo, lidar, thermal, radar, and sonar sensors fuses information in real-time for navigation, object recognition, localization, and classification.

Negative obstacle detection

The detection of negative hazards combines the use of three-dimensional point cloud data with electro-optical imagery, such as color or thermal, to improve negative obstacle detection reliability. The reliable detection range for negative hazards determines the speed the vehicle can safely operate in dense, cluttered, off-road environments.


Deep Learning classifiers


Neya uses deep learning techniques to solve difficult perception and classification problems for autonomy.  We modify off-the-shelf networks or create and train new networks.

perception, computer vision, & Machine learning modules

Neya has a vast library of perception modules that can rapidly integrate into new applications.

  • Obstacle Detection on Land & Water
  • Negative Obstacle Detection
  • Airborne Surface Estimation
  • Depth Estimation Given a Single Image
  • Sky/Land/Water Segmentation using Deep Learning
  • Object & Terrain Classification
  • Object Tracking & Localization
  • Image Enhancement & Upscaling

Perception projects

Combat Vehicle Robotics

Autonomous Ground Re-Supply