Light Robust Monocular Depth Estimation For Outdoor Environment Via Monochrome And Color Camera Fusion
This work addresses depth estimation for applications like autonomous driving by improving robustness to light conditions, though it is incremental as it builds on existing sensor fusion methods.
The paper tackles the problem of monocular depth estimation in outdoor environments by fusing color and monochrome camera images, achieving state-of-the-art performance across all metrics while being efficient in cost, memory, and computation.
Depth estimation plays a important role in SLAM, odometry, and autonomous driving. Especially, monocular depth estimation is profitable technology because of its low cost, memory, and computation. However, it is not a sufficiently predicting depth map due to a camera often failing to get a clean image because of light conditions. To solve this problem, various sensor fusion method has been proposed. Even though it is a powerful method, sensor fusion requires expensive sensors, additional memory, and high computational performance. In this paper, we present color image and monochrome image pixel-level fusion and stereo matching with partially enhanced correlation coefficient maximization. Our methods not only outperform the state-of-the-art works across all metrics but also efficient in terms of cost, memory, and computation. We also validate the effectiveness of our design with an ablation study.