Improving Monocular Depth Prediction in Ambiguous Scenes Using a Single Range Measurement

Abstract Depth maps are widely used in robotics, with numerous applications in agricultural tasks. Methods for estimating these from monocular images currently exist, but this is an ill posed problem which requires assumptions about object scale and camera focal length. These assumptions may not always be reasonable and how to deal with them has not been sufficiently explored in the current literature. For example, scenes in agriculture frequently violate the assumption of having a single scale per object class and represent a failure case for these methods. To avoid these assumptions when estimating depth maps, we present an approach where a single actual distance measurement is fused with a monocular image. Our results indicate that this method can outperform an image-only baseline, provided the distance measurement is sampled according to a projective model. We also found that a single measurement can significantly improve accuracy on simulated variable scale versions of two common public datasets. A hardware implementation of this approach was tested in an agricultural setting, though results were poor. Software and hardware designs are made available1.