Visual-Inertial Monocular SLAM With Map Reuse
| Autor: | Juan D. Tardós, Raul Mur-Artal |
|---|---|
| Rok vydání: | 2017 |
| Předmět: |
FOS: Computer and information sciences
0209 industrial biotechnology Control and Optimization Inertial frame of reference Computer science Computer Vision and Pattern Recognition (cs.CV) Computer Science - Computer Vision and Pattern Recognition Biomedical Engineering Initialization 02 engineering and technology Simultaneous localization and mapping Reuse Accelerometer 01 natural sciences law.invention Computer Science - Robotics 020901 industrial engineering & automation Odometry Artificial Intelligence Inertial measurement unit Robustness (computer science) law Computer vision business.industry Mechanical Engineering 010401 analytical chemistry Gyroscope 0104 chemical sciences Computer Science Applications Human-Computer Interaction Control and Systems Engineering Computer Vision and Pattern Recognition Artificial intelligence business Robotics (cs.RO) |
| Zdroj: | IEEE Robotics and Automation Letters. 2:796-803 |
| ISSN: | 2377-3774 |
| DOI: | 10.1109/lra.2017.2653359 |
| Popis: | In recent years there have been excellent results in Visual-Inertial Odometry techniques, which aim to compute the incremental motion of the sensor with high accuracy and robustness. However these approaches lack the capability to close loops, and trajectory estimation accumulates drift even if the sensor is continually revisiting the same place. In this work we present a novel tightly-coupled Visual-Inertial Simultaneous Localization and Mapping system that is able to close loops and reuse its map to achieve zero-drift localization in already mapped areas. While our approach can be applied to any camera configuration, we address here the most general problem of a monocular camera, with its well-known scale ambiguity. We also propose a novel IMU initialization method, which computes the scale, the gravity direction, the velocity, and gyroscope and accelerometer biases, in a few seconds with high accuracy. We test our system in the 11 sequences of a recent micro-aerial vehicle public dataset achieving a typical scale factor error of 1% and centimeter precision. We compare to the state-of-the-art in visual-inertial odometry in sequences with revisiting, proving the better accuracy of our method due to map reuse and no drift accumulation. Accepted for publication in IEEE Robotics and Automation Letters |
| Databáze: | OpenAIRE |
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