Introduction

• SLAM systems
• RGBD

References:

Primitive extraction

• Fast Cylinder and Plane Extraction from Depth Cameras for Visual Odometry
• Fast Sampling Plane Filtering, Polygon Construction and Merging from Depth Images
• Fast Plane Extraction in Organized Point Clouds Using Agglomerative HierarchicalClustering
• Depth image-based plane detection

Visual odometry

• LVT: Lightweight Visual Odometry for Autonomous Mobile Robots
• Dense RGB-D visual odometry using inverse depth
• SPLODE: Semi-Probabilistic Point and Line Odometry with Depth Estimation from RGB-D Camera Motion
• PL-SVO: Semi-Direct Monocular Visual Odometry byCombining Points and Line Segments
• Probabilistic Combination of Noisy Points andPlanes for RGB-D Odometry and Probabilistic RGB-D Odometry based on Points, Linesand Planes Under Depth Uncertainty
• Robust Stereo Visual Inertial Navigation System Based on Multi-Stage Outlier Removal in Dynamic Environments

SLAM

• ORB-SLAM2: an Open-Source SLAM System for Monocular, Stereo and RGB-D Cameras
• 3D SLAM in texture-less environments using rank order statistics
• 3D Mapping with an RGB-D Camera
• Online 3D SLAM by Registration of Large PlanarSurface Segments and Closed Form Pose-GraphRelaxation
• LIPS: LiDAR-Inertial 3D Plane SLAM
• Pop-up SLAM: Semantic Monocular Plane SLAM for Low-texture Environments
• Monocular Object and Plane SLAM in Structured Environments