A Benchmark Comparison of Monocular Visual-Inertial Odometry Algorithms for Flying Robots
By Jeffrey A. Delmerico, Davide Scaramuzza. IEEE International Conference on Robotics and Automation, 2018
399 citations recorded by the source corpus.
Subfields: Aerospace Engineering, Computer Vision and Pattern Recognition, Geology
View Paper: https://www.semanticscholar.org/paper/4e6d4a677d6bd1baf53ba0371b12aa7c76c367e1
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
- Davide Scaramuzza
- Guoquan Huang
- Shaojie Shen
- Luca Carlone
- Vladlen Koltun
- Rui Wang
- Tong Qin
- Daniel Cremers
In Computer Vision and Pattern Recognition
- Davide Scaramuzza
- Vladlen Koltun
- Daniel Cremers
- Shaojie Shen
- Luca Carlone
- Guoquan Huang
- Rui Wang
- René Ranftl
In Geology
- Shaojie Shen
- Vladlen Koltun
- Davide Scaramuzza
- Luca Carlone
- Tong Qin
- Henri Rebecq
- Rui Wang
- Guoquan Huang
Citations in the corpus
- LVI-SAM: Tightly-coupled Lidar-Visual-Inertial Odometry via Smoothing and Mapping (2021, background): “VINS-Mono performs non-linear optimization in a sliding window setting and achieves stateof-the-art accuracy with a monocular camera [8].”
- Visual-Inertial Navigation: A Concise Review (2019, background): “With the recent advancements of hardware design and manufacturing, low-cost light-weight micro-electro-mechanical (MEMS) IMUs have become ubiquitous [3, 4, 5], which enables high-accuracy localization for, among others, mobile devices [6] and micro aerial vehicles (MAVs) [7, 8, 9, 10, 11], holding h”
- A Comprehensive Survey of Visual SLAM Algorithms (2022, methodology): “Delmerico and Scaramuzza [65] used different hardware platforms based on CPU architectures to implement visual-inertial SLAM algorithms.”
- ORB-SLAM3: An Accurate Open-Source Library for Visual, Visual–Inertial, and Multimap SLAM (2020, neutral): “The qualitative accuracy and robustness ratings included in the table are based, for modern systems, on the comparisons reported in section VII, and for classical systems, on previous comparisons in the literature [2], [52].”
- D3VO: Deep Depth, Deep Pose and Deep Uncertainty for Monocular Visual Odometry (2020, result): “We refer to [9] for the results of the M+I methods.”
- VINS-Mono: A Robust and Versatile Monocular Visual-Inertial State Estimator (2017, background): “More benchmark comparisons can be found in [44], which shows a favorable performance of the proposed system comparing against other state-of-the-art algorithms.”
- Kimera: From SLAM to spatial perception with 3D dynamic scene graphs (2021, methodology): “2015) using the independently reported values in (Delmerico and Scaramuzza 2018) and the self-reported values in (Qin et al.”
- Kimera: an Open-Source Library for Real-Time Metric-Semantic Localization and Mapping (2019, methodology): “Using a Sim(3) alignment, as in [77], would result in an even smaller error for Kimera: we preferred the SE(3)”
- Events-To-Video: Bringing Modern Computer Vision to Event Cameras (2019, methodology): “Here, we go one step further and directly apply an off-the-shelf VIO system (specifically, VINS-Mono [36], which is state-of-the-art [11]) to videos reconstructed from events using either our approach, MR, or HF, and evaluate against UltimateSLAM.”