Parse geometry from a line: Monocular depth estimation with partial laser observation

Many standard robotic platforms are equipped with at least a fixed 2D laser range finder and a monocular camera. Although those platforms do not have sensors for 3D depth sensing capability, knowledge of depth is an essential part in many robotics activities. Therefore, recently, there is an increasing interest in depth estimation using monocular images. As this task is inherently ambiguous, the data-driven estimated depth might be unreliable in robotics applications. In this paper, we have attempted to improve the precision of monocular depth estimation by introducing 2D planar observation from the remaining laser range finder without extra cost. Specifically, we construct a dense reference map from the sparse laser range data, redefining the depth estimation task as estimating the distance between the real and the reference depth. To solve the problem, we construct a novel residual of residual neural network, and tightly combine the classification and regression losses for continuous depth estimation. Experimental results suggest that our method achieves considerable promotion compared to the state-of-the-art methods on both NYUD2 and KITTI, validating the effectiveness of our method on leveraging the additional sensory information. We further demonstrate the potential usage of our method in obstacle avoidance where our methodology provides comprehensive depth information compared to the solution using monocular camera or 2D laser range finder alone.

Authors

• Yiyi Liao
• Lichao Huang
• Yue Wang 0020
• Sarath Kodagoda
• Yinan Yu
• Yong Liu 0007

Keywords

• Estimation
• Lasers
• Two dimensional displays
• Neural networks
• Robot sensing systems
• Three-dimensional displays
• Depth Estimation
• Partial Observation
• Monocular Depth Estimation
• Neural Network
• Density Map
• Sparse Data
• Residual Network
• Depth Information
• Reference Map
• Robotic Applications
• Obstacle Avoidance
• Regression Loss
• Laser Ranging
• Residual Neural Network
• Monocular Camera
• Monocular Images
• Root Mean Square Error
• Laser Scanning
• Convolutional Neural Network
• Deep Network
• Depth Values
• Deep Neural Network
• Sparse Observations
• Dense Depth
• Depth Map
• Direction Of Gravity
• Ground Truth Depth
• Residual Block
• Classification Loss
• Real Depth