Visual-inertial navigation, mapping and localization: A scalable real-time causal approach

By Eagle Jones, Stefano Soatto. Int. J. Robotics Res., 2011

509 citations recorded by the source corpus.

Subfields: Aerospace Engineering, Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/53226f929c8affbf49c1bdb4cbe5e8ff1b05e0d4

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In Aerospace Engineering

In Computer Vision and Pattern Recognition

Citations in the corpus

Opportunities and challenges with autonomous micro aerial vehicles (2012, methodology): “The nonlinear observability analysis with applications to mapping is reported in Jones and Soatto (2011).”
Keyframe-Based Visual-Inertial SLAM using Nonlinear Optimization (2013, unknown): “Mourikis and Roumeliotis [14] proposed an EKF-based real-time fusion using monocular vision, while Jones and Soatto [8] presented mono-visual-inertial filtering results on a long outdoor trajectory including IMU to camera calibration and loop closure.”
PL-VIO: Tightly-Coupled Monocular Visual–Inertial Odometry Using Point and Line Features (2018, background): “Filtering-based approaches [19,20] integrate IMU measurements to propagate/predict the state, and then update/correct the latest state with visual measurements.”
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].”
High-precision, consistent EKF-based visual-inertial odometry (2013, methodology): “Most present-day algorithms in this class are either extended Kalman filter (EKF)-based methods (Mourikis and Roumeliotis, 2007; Jones and Soatto, 2011; Kelly and Sukhatme, 2011), or methods utilizing iterative minimization over a window of states (Konolige and Agrawal, 2008; Dong-Si and Mourikis,…”
Visual Semantic Navigation using Scene Priors (2018, background): “…where it is assumed that either the map of the environment is known a priori, e.g., Matthies & Shafer (1987); Borenstein & Koren (1991); Meng & Kak (1993); Kim & Nevatia (1999) or a map is built on the ﬂy e.g., Thrun (1998); Feder et al. (1999); Jones & Soatto (2011); Siagian et al. (2014).”
Estimation, Control, and Planning for Aggressive Flight With a Small Quadrotor With a Single Camera and IMU (2017, methodology): “The minimal sensor suite for autonomous localization consists of two inexpensive, lightweight and widely available sensors, a monocular camera and an IMU as shown in [12]–[15].”
Ultimate SLAM? Combining Events, Images, and IMU for Robust Visual SLAM in HDR and High-Speed Scenarios (2017, unknown): “A example approach of a filter-based visualinertial odometry system is [15].”
Direct visual-inertial odometry with stereo cameras (2016, methodology): “While the complementary nature of these two approaches has long been known [8], the energy-based approach [20] [16] [17], – which we employ in this paper – allows for easily and adaptively re-linearizing energy terms if required, thereby avoiding systematic error integration from linearization.”
Visual SLAM and Structure from Motion in Dynamic Environments (2018, background): “Other approaches leverage external sensors such as an inertial measurement unit (IMU) to solve this problem [67, 92] by estimating the camera ego-motion.”
Robust visual inertial odometry using a direct EKF-based approach (2015, methodology): “[5], additional IMU measurements can be relatively simply integrated into the ego-motion estimation, whereby calibration parameters can be co-estimated online [14], [12].”
Visual-Inertial Navigation: A Concise Review (2019, background): “For example, Jones and Soatto [128], Hernandez et al. [153] the system’s indistinguishable trajectories [154] were examined from the observability perspective.”
Tightly Coupled 3D Lidar Inertial Odometry and Mapping (2019, methodology): “With the provided lidar poses, sufficient motions of the sensor pair are required to make the IMU states observable [14].”
Vision based navigation for micro helicopters (2012, methodology): “The approach presented in this section is based on the visual SLAM algorithm PTAM of Klein and Murray (Klein and Murray (2007)) in order to localize the MAV with a single camera in 6DoF (see Fig.”
Vision-based state estimation for autonomous rotorcraft MAVs in complex environments (2013, background): “A nonlinear observability analysis of the estimation problem shows the presence of unobservable modes that can only be eliminated through motions that involve non-zero linear accelerations [4, 5].”
Optic-Flow Based Control of a 46g Quadrotor (2013, background): “One of the most successful solution is inertial SLAM (Simultaneous Localization and Mapping) [2], [3], [4], [5],”
Visual-Inertial Monocular SLAM With Map Reuse (2016, methodology): “The filtering method [9], is able to close loops topologically and reuse its map, but global metric consistency is not enforced in real-time.”
Vision and IMU Data Fusion: Closed-Form Solutions for Attitude, Speed, Absolute Scale, and Bias Determination (2012, background): “In [11], a theoretical investigation to analytically derive the motion conditions under which the vehicle state is observable is provided.”