Depth Estimation with Ego-Motion Assisted Monocular Camera

Abstract

We propose a method to estimate the distance to objects based on the complementary nature of monocular image sequences and camera kinematic parameters. The fusion of camera measurements with the kinematics parameters that are measured by an IMU and an odometer is performed using an extended Kalman filter. Results of field experiments with a wheeled robot corroborated the results of the simulation study in terms of accuracy of depth estimation. The performance of the approach in depth estimation is strongly affected by the mutual observer and feature point geometry, measurement accuracy of the observer’s motion parameters and distance covered by the observer. It was found that under favorable conditions the error in distance estimation can be as small as 1% of the distance to a feature point. This approach can be used to estimate distance to objects located hundreds of meters away from the camera.