Our perception framework relies on heterogeneous sensors, through advanced fusion algorithms to create a highly accurate 2.5 dynamic occupancy grid. The map-based localization feature utilizes visual cues, Inertial Navigation Unit (INU) pose data, and wheel odometry, ensuring precise pose information with an exceptional accuracy of less than 5 cm even in tight and narrow pathways.

Environment Perception

  • OGM Generation using planar and 3D LiDARs and cameras
  • Co-ordinate transformation
  • Configurable resolution, and radial distance
  • Configurable exclusion zone
  • 3D to 2D projection with spatial partitioning
  • Configurable z axis cut-offs
  • Probabilistic and Binary OGM generation with 3D
  • Configurable potential field for obstacles
  • Occluded, free, occupied zones can be extracted
  • Drivable region extraction
  • Object classification and tracking
  • Obstacle list generation
  • Lead subject tracking
  • Threat prediction


  • Particle filter for pose tracking
  • <5 cm based on environment challenges in corridors
  • Filtering for random disturbances in measurements
  • Wheel odometry, Visual odometry, indoor GPS
  • Dynamically adjustable weights based on scenario