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

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Tong Qin , Jie Pan , Shaozu Cao , Shaojie Shen ·

YOU? · · 2019 · Open Access · · DOI: https://doi.org/10.48550/arxiv.1901.03638

Nowadays, more and more sensors are equipped on robots to increase robustness and autonomous ability. We have seen various sensor suites equipped on different platforms, such as stereo cameras on ground vehicles, a monocular camera with an IMU (Inertial Measurement Unit) on mobile phones, and stereo cameras with an IMU on aerial robots. Although many algorithms for state estimation have been proposed in the past, they are usually applied to a single sensor or a specific sensor suite. Few of them can be employed with multiple sensor choices. In this paper, we proposed a general optimization-based framework for odometry estimation, which supports multiple sensor sets. Every sensor is treated as a general factor in our framework. Factors which share common state variables are summed together to build the optimization problem. We further demonstrate the generality with visual and inertial sensors, which form three sensor suites (stereo cameras, a monocular camera with an IMU, and stereo cameras with an IMU). We validate the performance of our system on public datasets and through real-world experiments with multiple sensors. Results are compared against other state-of-the-art algorithms. We highlight that our system is a general framework, which can easily fuse various sensors in a pose graph optimization. Our implementations are open source\footnote{https://github.com/HKUST-Aerial-Robotics/VINS-Fusion}.

Related Topics

Inertial Measurement Unit

Odometry

Artificial Intelligence

Simultaneous Localization And Mapping

Fuse (Electrical)

General-Purpose Machine Gun

Electrical Engineering

Concepts

Inertial measurement unit Odometry Artificial intelligence Computer vision Computer science Robustness (evolution) Monocular Robotics Robot Factor graph Image sensor Simultaneous localization and mapping Fuse (electrical) Pose Mobile robot Engineering Decoding methods Biochemistry Telecommunications Chemistry Electrical engineering Gene

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Type: preprint
Language: en
Landing Page: http://arxiv.org/abs/1901.03638
PDF: https://arxiv.org/pdf/1901.03638
OA Status: green
Cited By: 296
References: 29
Related Works: 10
OpenAlex ID: https://openalex.org/W2909063396

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DOI: https://doi.org/10.48550/arxiv.1901.03638

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

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Type: preprint

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Language: en

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Publication year: 2019

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Publication date: 2019-01-11

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Authors: Tong Qin, Jie Pan, Shaozu Cao, Shaojie Shen

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Landing page: https://arxiv.org/abs/1901.03638

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PDF URL: https://arxiv.org/pdf/1901.03638

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Concepts: Inertial measurement unit, Odometry, Artificial intelligence, Computer vision, Computer science, Robustness (evolution), Monocular, Robotics, Robot, Factor graph, Image sensor, Simultaneous localization and mapping, Fuse (electrical), Pose, Mobile robot, Engineering, Decoding methods, Biochemistry, Telecommunications, Chemistry, Electrical engineering, Gene

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Cited by: 296

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Citations by year (recent): 2025: 13, 2024: 57, 2023: 54, 2022: 42, 2021: 66

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abstract_inverted_index.robots.	52
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abstract_inverted_index.various	18, 196
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abstract_inverted_index.cameras,	146
abstract_inverted_index.choices.	87
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abstract_inverted_index.datasets	168
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abstract_inverted_index.source\footnote{https://github.com/HKUST-Aerial-Robotics/VINS-Fusion}.	207
cited_by_percentile_year
countries_distinct_count	0
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citation_normalized_percentile