Robust visual inertial odometry using a direct EKF-based approach

By Michael Bloesch, Sammy Omari, Marco Hutter, Roland Yves Siegwart. 2015

924 citations recorded by the source corpus.

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

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Citations in the corpus

Beauty and the Beast: Optimal Methods Meet Learning for Drone Racing (2018, methodology): “Traditional approaches to autonomous MAV navigation build on visual inertial odometry (VIO) [5], [1], [13], [24] or simultaneous localization and mapping (SLAM) [21], [23], which are used to provide a pose estimate of the drone relative to an internal metric map [14], [4].”
Kimera: an Open-Source Library for Real-Time Metric-Semantic Localization and Mapping (2019, methodology): “First, while Back-end Geometry Sema-ntics ORB-SLAM [22] mono g2o points DSO [23] mono g2o points VINS-mono [24] mono/IMU Ceres points VINS-Fusion [25] mono/Stereo/IMU Ceres points ROVIOLI [26] stereo/IMU EKF points ElasticFusion [18] RGB-D alternation surfels Voxblox [27] RGB-D [26] TSDF SLAM++ [16]”
Visual-Inertial Teach and Repeat for Aerial Inspection (2018, background): “{marius.fehr, thomas.schneider, marcin.dymczyk}@mavt.ethz.ch, rsiegwart@ethz.ch 2Google Inc.: jsturm@google.com This reasearch was funded by Google Tango. maplab [7], ORB-SLAM2 [8], OKVIS [9], ROVIO [10] and the visual-inertial navigation system presented in [11] autonomous robots are able to perfor”
ORB-SLAM3: An Accurate Open-Source Library for Visual, Visual–Inertial, and Multimap SLAM (2020, methodology): “While these systems rely on features, ROVIO [41], [42] feeds an EFK with photometric error using direct data association.”
Maplab: An Open Framework for Research in Visual-Inertial Mapping and Localization (2017, methodology): “4: Modules and data flows within ROVIOLI (ROVIO [3] with Localization Integration).”
Survey on Computer Vision for UAVs: Current Developments and Trends (2017, methodology): “In [85] a monocular visual inertial odometry algorithm has been presented.”
Receding Horizon "Next-Best-View" Planner for 3D Exploration (2016, methodology): “A point cloud of the environment is constructed from the stereo image for the environment mapping, while a visual–inertial odometry algorithm is used for pose estimation [28], [29].”
A Comprehensive Survey of Visual SLAM Algorithms (2022, methodology): “The Robust Visual Inertial Odometry (ROVIO) algorithm [69] is another ﬁlter-based method that uses the EKF approach, and similar to other ﬁlter-based methods, it uses the IMU data to state propagation, and the camera data to ﬁlter update.”
On-Manifold Preintegration for Real-Time Visual--Inertial Odometry (2015, background): “The complexity of the EKF grows quadratically in the number of estimated landmarks, therefore, a small number of landmarks (in the order of 20) are typically tracked to allow real-time operation [10–12].”
Visual-Inertial Monocular SLAM With Map Reuse (2016, methodology): “This is due to the marginalization of past states to maintain a constant computational cost [1]– [3], [5], [6], or the use of full smoothing [4], [7], with an almost constant complexity in exploration but that can be as expensive as a batch method in the presence of loop closures.”
Direct visual-inertial odometry with stereo cameras (2016, background): “Two main categories can be identified: Filtering-based approaches [21] [2] [29] operate on a probabilistic state representation – mean and covariance – in a Kalman-filtering framework.”
Adaptive VIO: Deep Visual-Inertial Odometry with Online Continual Learning (2024, methodology): “Filter based methods, such as MSCKF [17, 22], and ROVIO [2], use the extended Kalman filter (EKF) to propagate and up date the current state.”
A Survey on Active Simultaneous Localization and Mapping: State of the Art and New Frontiers (2022, background): “[46] ROVIO [47] & mapping Stereo & IMU Octomap MPC RRT paths Coverage & distance Sim.”
Extending Monocular Visual Odometry to Stereo Camera Systems by Scale optimization (2019, positive): “To solve the scale problem without intensive computational cost, authors [4, 5, 6] have fused monocular VO with an inertial measurement unit (IMU) to create visual-inertial navigation systems (VINS).”
Iterated extended Kalman filter based visual-inertial odometry using direct photometric feedback (2017, methodology): “In our previous work (Bloesch et al. (2015)) we ﬁxed the patch size to 8 × 8 .”
Robot Parkour Learning (2023, methodology): “Classical modular methods rely on decoupled visual perception and control pipelines, where the elevation maps [75, 76, 77, 78, 79, 80, 81], traversability maps [82, 83, 84, 85], or state estimators [86, 87, 88, 89, 90, 91, 92, 93, 94, 95] are constructed as intermediate representations for downstrea”
Robust Stereo Visual Inertial Odometry for Fast Autonomous Flight (2017, methodology): “We compare our results on the EuRoC dataset with three representative VIO systems, OKVIS (stereo-optimization), ROVIO (monocular-ﬁlter), and VINS-MONO (monocular-optimization).”
Visual-Inertial Navigation: A Concise Review (2019, methodology): “[61, 62] used a patch-based direct method to provide updates with an iterated EKF; Usenko et al.”