An odometry estimation method for micro indoor flying robot with lightweight sensors
Micro-quadrotor usually equips with lightweight sensors due to its limited payload. In this paper, an odometry estimation method based on 2D laser scanner and monocular vision is proposed to solve the pose estimation problem of micro-quadrotor flying robot in indoor environments. This method firstly detects and matches image features of current and previous frames to get all feature correspondences to calculate the monocular odometry. Then, laser scans are transformed into image coordinate system using sensor calibration parameters. After that, the unknown translation scale of monocular odometry is determined by using triangulation. At the same time, line features of laser scans are extracted and the line angle histogram is computed. If the angle distribution is wide enough, which means it is likely to be in a structural environment, a scan matching algorithm is used to calculate the laser odometry. Finally, estimation results based on the two different sensors are fused by using weighted average method. Experiment results show that the proposed method can recover the trajectory of the robot robustly and accurately. The close loop error is less than 1% for a 64 m loop around indoor corridor.