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.

中文翻译：

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自我运动辅助单眼相机的深度估计

摘要—

我们提出了一种基于单眼图像序列和相机运动学参数的互补性来估计到物体的距离的方法。使用扩展的卡尔曼滤波器将相机测量值与运动学参数（由IMU和里程表测量）融合在一起。就深度估算的准确性而言，轮式机器人的现场实验结果证实了仿真研究的结果。该方法在深度估计中的性能受到相互观察者和特征点几何形状，观察者运动参数的测量精度以及观察者所覆盖距离的强烈影响。已经发现，在有利的条件下，距离估计中的误差可以小到到特征点的距离的1％。