SLAM, Self-Localization and Autonomous Navigation

Tyche moves autonomously, avoid obstacle, explore the unknown space and create virtual map, estimate its own location in the virtual map simultaneously.

1)IR sensor: to sense forward to detect object in front of tyche

2)IMU sensor:

  • based on android built-in gyro sensor and tyche wheel encoder)
  • to estimate robot position and orientation (2D pose) relative to the initial state.
  • (good) its computation cost is very small, with good performance
  • (bad) the drift error will be accumulated and it can not build space map, its blind.

3)Visual sensor:

  • Extra camera module with 320×240 @30 fps image sensor, FOV should be 120 degrees
  • to estimate robot pose and extract 3D features from environment simultaneously.
  • (good) it create 3d point cloud from environment. The map can be created based on it.
  • (bad) both computing cost and memory cost is very heavy.
  • Fuse IMU and visual data to generate 3d point clouds as sparse sub-maps.
  • match and merge sub-maps / optimize the 3d point could
  • Create high-level virtual map with user annotation (from verbal input or other methods)
  • Application with AI

SLAM Visualization

  • Implement a 3D visualization of simultaneous localization and mapping (SLAM) based on raw sensor data provided by Tyche with Android based cellphone.
  • Implements a particle filter to track the robot trajectories.


  • Sensor data
  • Coordinate frames
  • Maps being built

Current status: Waiting for testing with real data