A Survey of Simultaneous Localization and Mapping with an Envision in 6G Wireless Networks
arXiv:1909.05214
Abstract
Simultaneous Localization and Mapping (SLAM) achieves the purpose of simultaneous positioning and map construction based on self-perception. The paper makes an overview in SLAM including Lidar SLAM, visual SLAM, and their fusion. For Lidar or visual SLAM, the survey illustrates the basic type and product of sensors, open source system in sort and history, deep learning embedded, the challenge and future. Additionally, visual inertial odometry is supplemented. For Lidar and visual fused SLAM, the paper highlights the multi-sensors calibration, the fusion in hardware, data, task layer. The open question and forward thinking with an envision in 6G wireless networks end the paper. The contributions of this paper can be summarized as follows: the paper provides a high quality and full-scale overview in SLAM. It's very friendly for new researchers to hold the development of SLAM and learn it very obviously. Also, the paper can be considered as a dictionary for experienced researchers to search and find new interesting orientation.
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References in corpus (47)
- VINS-Mono: A Robust and Versatile Monocular Visual-Inertial State Estimator
- PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
- PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Space
- On-Manifold Preintegration for Real-Time Visual-Inertial Odometry
- DS-SLAM: A Semantic Visual SLAM towards Dynamic Environments
- RTAB-Map as an Open-Source Lidar and Visual SLAM Library for Large-Scale and Long-Term Online Operation
- DeepVO: Towards End-to-End Visual Odometry with Deep Recurrent Convolutional Neural Networks
- Visual-Inertial Monocular SLAM with Map Reuse
- Tightly Coupled 3D Lidar Inertial Odometry and Mapping
- Deep Continuous Fusion for Multi-Sensor 3D Object Detection
- Online Global Loop Closure Detection for Large-Scale Multi-Session Graph-Based SLAM
- SfM-Net: Learning of Structure and Motion from Video
- CCNet: Criss-Cross Attention for Semantic Segmentation
- PointSIFT: A SIFT-like Network Module for 3D Point Cloud Semantic Segmentation
- OpenVSLAM: A Versatile Visual SLAM Framework
- maplab: An Open Framework for Research in Visual-inertial Mapping and Localization
- CalibNet: Geometrically Supervised Extrinsic Calibration using 3D Spatial Transformer Networks
- Co-Fusion: Real-time Segmentation, Tracking and Fusion of Multiple Objects
- LiDAR-Camera Calibration using 3D-3D Point correspondences
- DeepV2D: Video to Depth with Differentiable Structure from Motion
- Robust Dense Mapping for Large-Scale Dynamic Environments
- Estimating Depth from RGB and Sparse Sensing
- GeoNet: Unsupervised Learning of Dense Depth, Optical Flow and Camera Pose
- Propagating Confidences through CNNs for Sparse Data Regression
- InfiniTAM v3: A Framework for Large-Scale 3D Reconstruction with Loop Closure
- ENFT: Efficient Non-Consecutive Feature Tracking for Robust Structure-from-Motion
- Event-based Gesture Recognition with Dynamic Background Suppression using Smartphone Computational Capabilities
- ProSLAM: Graph SLAM from a Programmer's Perspective
- Semi-Dense 3D Semantic Mapping from Monocular SLAM
- Toward Geometric Deep SLAM
- DeepVO: A Deep Learning approach for Monocular Visual Odometry
- Event-based Camera Pose Tracking using a Generative Event Model
- Radio Localization and Mapping with Reconfigurable Intelligent Surfaces
- Lightweight Unsupervised Deep Loop Closure
- YOLO3D: End-to-end real-time 3D Oriented Object Bounding Box Detection from LiDAR Point Cloud
- GCNv2: Efficient Correspondence Prediction for Real-Time SLAM
- MultiCol-SLAM - A Modular Real-Time Multi-Camera SLAM System
- Towards 6G Networks: Use Cases and Technologies
- Robust Keyframe-based Dense SLAM with an RGB-D Camera
- MID-Fusion: Octree-based Object-Level Multi-Instance Dynamic SLAM
- TagSLAM: Robust SLAM with Fiducial Markers
- LiDAR and Camera Calibration using Motion Estimated by Sensor Fusion Odometry
- LIC-Fusion: LiDAR-Inertial-Camera Odometry
- LIMO: Lidar-Monocular Visual Odometry
- Stereo Visual Inertial LiDAR Simultaneous Localization and Mapping
- CNN-SVO: Improving the Mapping in Semi-Direct Visual Odometry Using Single-Image Depth Prediction
- Point-Based Multi-View Stereo Network