.png){kind=icon}
Return
Improving Monocular Visual Odometry Using Learned Depth
.png){kind=icon}
OA
AI
DOI：10.1109/TRO.2022.3164834.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Monocular visual odometry (VO) is an important task in robotics and computer vision. Thus far, how to build accurate and robust monocular VO systems that can work well in diverse scenarios remains largely unsolved. In this article, we propose a framework to exploit monocular depth estimation for improving VO. The core of our framework is a monocular depth estimation module with a strong generalization capability for diverse scenes. It consists of two separate working modes to assist the localization and mapping. With a single monocular image input, the depth estimation module predicts a relative depth to help the localization module on improving the accuracy. With a sparse depth map and an RGB image input, the depth estimation module can generate accurate scale-consistent depth for dense mapping. Compared with current learning-based VO methods, our method demonstrates a stronger generalization ability to diverse scenes. More significantly, our framework is able to boost the performances of existing geometry-based VO methods by a large margin.
Keywords:
Estimation
Training
Cameras
Sensors
Location awareness
Sun
Simultaneous localization and mapping
Monocular depth estimation
simultaneous localization and mapping (SLAM)
visual odometry (VO)
Journal
IF:
10.5
Papers:
3.3K
Citations:
2.8W
Organization
Cited Papers
Synthesis, characterization, and electrochemical behavior of a new Nd1.9Sr0.1Ni0.9Co0.1O4 ± δ material as electrocatalyst for the oxygen reduction reaction.png)
Ionics
IonicsIF0
PREAI
ShareSaveCarboxylated pillar[n]arene (n= 5–7) host molecules: high affinity and selective binding in water
PREAI
ShareSaveShareSaveHexachlorocyclohexane and Dichlorodiphenyltrichloroethane Residues in the Dustfall of Tianjin, China
PREAI
ShareSaveShareSaveShareSave
View more