Scale Recovery for Monocular Visual Odometry Using Depth Estimated With Deep Convolutional Neural Fields
Scale recovery is one of the central problems for monocular visual odometry. Normally, road plane and camera height are specified as reference to recover the scale. The performances of these methods depend on the plane recognition and height measurement of camera. In this work, we propose a novel method to recover the scale by incorporating the depths estimated from images using deep convolutional neural fields. Our method considers the whole environmental structure as reference rather than a specified plane. The accuracy of depth estimation contributes to the scale recovery. We improve the performance of depth estimation by considering two consecutive frames and egomotion of camera into our networks. The depth refinement and scale recovery are obtained iteratively. In this way, our method can eliminate the scale drift and improve the depth estimation simultaneously. The effectiveness of our method is verified on the KITTI dataset for both visual odometry and depth estimation tasks.
Code (0)
등록된 구현이 없습니다.
Tasks
Depth EstimationMonocular Visual OdometryVisual OdometrySimilar Papers 제목 키워드 기반
UnDeepVO: Monocular Visual Odometry through Unsupervised Deep Learning
We propose a novel monocular visual odometry (VO) system called UnDeepVO in this paper. UnDeepVO is able to estimate the 6-DoF pose of a monocular camera and the depth of its view by using deep neural networks. There are…
Deep LearningMonocular Visual OdometryVisual OdometryDense Prediction Transformer for Scale Estimation in Monocular Visual Odometry
Monocular visual odometry consists of the estimation of the position of an agent through images of a single camera, and it is applied in autonomous vehicles, medical robots, and augmented reality. However, monocular syst…
Autonomous VehiclesMonocular Visual OdometryPositionVisual OdometryCodedVO: Coded Visual Odometry
Autonomous robots often rely on monocular cameras for odometry estimation and navigation. However, the scale ambiguity problem presents a critical barrier to effective monocular visual odometry. In this paper, we present…
Monocular Visual OdometryVisual OdometryDeep Virtual Stereo Odometry: Leveraging Deep Depth Prediction for Monocular Direct Sparse Odometry
Monocular visual odometry approaches that purely rely on geometric cues are prone to scale drift and require sufficient motion parallax in successive frames for motion estimation and 3D reconstruction. In this paper, we …
3D ReconstructionDepth EstimationDepth PredictionMonocular Visual Odometry+2Geometry-Constrained Monocular Scale Estimation Using Semantic Segmentation for Dynamic Scenes
Monocular visual localization plays a pivotal role in advanced driver assistance systems and autonomous driving by estimating a vehicle's ego-motion from a single pinhole camera. Nevertheless, conventional monocular visu…
Autonomous DrivingComputational EfficiencyMonocular Visual OdometryMotion Estimation+3