Papers › Visual-Inertial Monocular SLAM with Map Reuse

Visual-Inertial Monocular SLAM with Map Reuse

19 Oct 2016arXiv:1610.05949archive 2025-07-28

Raul Mur-Artal, Juan D. Tardos

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.

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13 repositories listed; official and paper-mentioned ones first.

CanCanZeng/LearnVIORB mentioned on GitHub report
WhutChengjun/VI-ORB mentioned on GitHub report
enteguo/LearnVIORBnorosgai2 mentioned on GitHub report
leavesnight/VIEO_SLAM mentioned on GitHubNOASSERTION report
lianbin/VIOSLAM mentioned on GitHub report
liguolinhit/VIORB mentioned on GitHub report
ns15417/LearnVIORB-RGBD mentioned on GitHub report
orbslam-project/VIORB mentioned on GitHub report
pangfumin/Learn_VIORB mentioned on GitHub report
tlglovewf/VIORB_FOR_STUDY mentioned on GitHub report

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Simultaneous Localization and Mapping

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