arXiv:1808.05336
Simultaneous Localization And Mapping with depth Prediction using Capsule Networks for UAVs
원문 초록
In this paper, we propose an novel implementation of a simultaneous localization and mapping (SLAM) system based on a monocular camera from an unmanned aerial vehicle (UAV) using Depth prediction performed with Capsule Networks (CapsNet), which possess improvements over the drawbacks of the more widely-used Convolutional Neural Networks (CNN). An Extended Kalman Filter will assist in estimating the position of the UAV so that we are able to update the belief for the environment. Results will be evaluated on a benchmark dataset to portray the accuracy of our intended approach.
함께 살펴볼 논문
공통 태그를 근거로 연결했습니다. 실제 인용 관계나 논문의 우수성을 뜻하지 않습니다.
DERD-Net: Learning Depth from Event-based Ray Densities
MonoMVSNet: Monocular Priors Guided Multi-View Stereo Network
Edge-Enabled VIO with Long-Tracked Features for High-Accuracy Low-Altitude IoT Navigation
Beyond Appearance: Geometric Cues for Robust Video Instance Segmentation
RoboScape: Physics-informed Embodied World Model
Posterior Cramér-Rao Bounds on Localization and Mapping Errors in Distributed MIMO SLAM
토론
이 논문과 연결된 포럼 글이 아직 없습니다.