3-D Mapping With an RGB-D Camera

By Felix Endres, Jürgen Hess, Jürgen Sturm, Daniel Cremers, Wolfram Burgard. 2014

816 citations recorded by the source corpus.

Subfields: Aerospace Engineering, Computer Vision and Pattern Recognition

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Aerospace Engineering

In Computer Vision and Pattern Recognition

Citations in the corpus

RGB-D datasets using microsoft kinect or similar sensors: a survey (2016, methodology): “The related performance comparisons between TUM Benchmark dataset and ICL-NUIM dataset are shown in [22] and [75].”
Robust reconstruction of indoor scenes (2015, methodology): “Prior work on scene reconstruction with consumer depth cameras recognized the importance of global registration [29, 18, 69, 62, 65].”
Computing Systems for Autonomous Driving: State of the Art and Challenges (2020, background): “Direct matching [112], [113], [114] is called dense visual SLAM, which adopts original information for matching that is dense in the image,”
Neural Point-Based Graphics (2019, methodology): “Robust algorithms for RGBD-based simultaneous localization and mapping (SLAM) are now available [12, 24, 42, 47].”
ORB-SLAM: A Versatile and Accurate Monocular SLAM System (2015, methodology): “We compare also with the trajectories generated by RGBDSLAM [43], which are provided for some of the sequences in the benchmark website.”
A Comprehensive Survey of Visual SLAM Algorithms (2022, background): “The RGBDSLAMv2 [96] is one of the most popular RGB-D-based algorithms and relies on feature extraction.”
Online Reconstruction of Indoor Scenes from RGB-D Streams (2016, background): “The importance of loop closure and map optimization for large scale RGB-D SLAM has been recognized by [12, 34, 13, 7, 32].”
PL-SLAM: Real-time monocular visual SLAM with points and lines (2017, methodology): “Before computing the error, all trajectories are aligned using a similarity warp except for the RGBD-SLAM [6] which is aligned by a rigid body transformation.”
RTAB‐Map as an open‐source lidar and visual simultaneous localization and mapping library for large‐scale and long‐term online operation (2018, methodology): “Besides RTAB ‐ Map and RGBDSLAMv2, no visual SLAM approaches provide out ‐ of ‐ the ‐ box occupancy grid outputs required for autonomous navigation.”
A Survey on Active Simultaneous Localization and Mapping: State of the Art and New Frontiers (2022, background): “[44] RGBDSLAM [45] RGB-D Octomap Modular Frontiers Coverage & distance Simulation ✗”
Colored Point Cloud Registration Revisited (2017, background): “Dense reconstruction from RGB-D sequences has been extensively studied [29, 17, 24, 10, 4, 41].”
Real-Time Large-Scale Dense 3D Reconstruction with Loop Closure (2016, methodology): “In some implementations (e.g. [20]), information from those frames not selected as keyframes is lost to the 3D modelling process, but other methods try to accumulate the non-keyframe information by improving or ﬁltering the keyframe measurements [21, 22, 19].”
ORB-SLAM2: An Open-Source SLAM System for Monocular, Stereo, and RGB-D Cameras (2016, methodology): “In Table III we compare our accuracy to the following state-of-the-art methods: ElasticFusion [15], Kintinuous [12], DVO-SLAM [14] and RGB-D SLAM [13].”