A General Optimization-based Framework for Local Odometry Estimation with Multiple Sensors

By Tong Qin, Jie Pan, Shaozu Cao, Shaojie Shen. arXiv.org, 2019

411 citations recorded by the source corpus.

Accurate state estimation is a fundamental problem for autonomous robots. To achieve locally accurate and globally drift-free state estimation, multiple sensors with complementary properties are usually fused together. Local sensors (camera, IMU, LiDAR, etc) provide precise pose within a small region, while global sensors (GPS, magnetometer, barometer, etc) supply noisy but globally drift-free localization in a large-scale environment. In this paper, we propose a sensor fusion framework to fuse local states with global sensors, which achieves locally accurate and globally drift-free pose estimation. Local estimations, produced by existing VO/VIO approaches, are fused with global sensors in a pose graph optimization. Within the graph optimization, local estimations are aligned into a global coordinate. Meanwhile, the accumulated drifts are eliminated. We evaluate the performance of our system on public datasets and with real-world experiments. Results are compared against other state-of-the-art algorithms. We highlight that our system is a general framework, which can easily fuse various global sensors in a unified pose graph optimization. Our implementations are open source\footnote{this https URL}.

Subfields: Aerospace Engineering, Computer Vision and Pattern Recognition, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/cf04ea26d9f92ce5ba823bdf70dba4ba5d61c2df

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Citations in the corpus

Kimera: an Open-Source Library for Real-Time Metric-Semantic Localization and Mapping (2019, neutral): “VINS-Fusion [25] mono/stereo/IMU Ceres points 7”
Adaptive VIO: Deep Visual-Inertial Odometry with Online Continual Learning (2024, methodology): “While nonlinear-optimization-based methods, like VINS [24, 25], ORB-SLAM3 [6] and DM VIO [30], adopt local visual-inertial bundle adjustment for the state estimation, achieving more accurate pose tracking.”
F-LOAM : Fast LiDAR Odometry and Mapping (2021, neutral): “Most state-ofthe-art SLAM methods have been evaluated on this dataset, e.g., ORB-SLAM [24], VINS-Fusion [25], LIMO [26], and LSD-SLAM [27].”
NeuralRecon: Real-Time Coherent 3D Reconstruction from Monocular Video (2021, background): “While camera motion can be tracked accurately with state-of-the-art visual-inertial SLAM systems [3, 35, 1], real-time image-based dense reconstruction remains to be a challenging problem due to low reconstruction quality and high computation demands.”
Collaborative Multi-Robot Search and Rescue: Planning, Coordination, Perception, and Active Vision (2020, background): “This is the case, for instance, of visual-inertial odometry with either monocular cameras [113], or multiple sensors [114].”
ORB-SLAM3: An Accurate Open-Source Library for Visual, Visual–Inertial, and Multimap SLAM (2020, positive): “In VINS-Fusion [44] it has been extended to stereo and stereo-inertial.”
OpenVINS: A Research Platform for Visual-Inertial Estimation (2020, methodology): “Note that we evaluate only the VIO portion of these code-bases (i.e., not the non-realtime backend pose graph thread output of VINS-Fusion [3] and visual-inertial mapping of Basalt [4]), as one could simply append a pose graph optimizer after any of these odometry methods to improve long-term…”