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Found 5 papers, 3 papers with code
RISE-SLAM: A Resource-aware Inverse Schmidt Estimator for SLAM
In this paper, we present the RISE-SLAM algorithm for performing visual-inertial simultaneous localization and mapping (SLAM), while improving estimation consistency.
Computational Efficiency
Simultaneous Localization and Mapping
Deep Multi-view Depth Estimation with Predicted Uncertainty
In this paper, we address the problem of estimating dense depth from a sequence of images using deep neural networks.
Deep Depth Estimation from Visual-Inertial SLAM
This paper addresses the problem of learning to complete a scene's depth from sparse depth points and images of indoor scenes.
Surface Normal Estimation of Tilted Images via Spatial Rectifier
Our two main hypotheses are: (1) visual scene layout is indicative of the gravity direction; and (2) not all surfaces are equally represented by a learned estimator due to the structured distribution of the training data, thus, there exists a transformation for each tilted image that is more responsive to the learned estimator than others.
Alternating-Stereo VINS: Observability Analysis and Performance Evaluation
One approach to improve the accuracy and robustness of vision-aided inertial navigation systems (VINS) that employ low-cost inertial sensors, is to obtain scale information from stereoscopic vision.
