Supplementary Material to: IMU Preintegration on Manifold for Efficient Visual-Inertial Maximum-a-Posteriori Estimation

By Christian Forster, Luca Carlone, Frank Dellaert, Davide Scaramuzza. 2015

478 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/fc0728de40458f0ade2b4976a79d8c7ad0c057fe

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Citations in the corpus

VINS-Mono: A Robust and Versatile Monocular Visual-Inertial State Estimator (2017, methodology): “We follow our previous continuous-time quaternion-based derivation of IMU preintegration [16], and include the handling of IMU biases as [19] and [24].”
Autonomous aerial navigation using monocular visual‐inertial fusion (2018, unknown): “⋮ ⋮ ∑N j=0 ηMj(t − M−1)j M−1 ≤ t ≤ M, (34)”
AutoRally: An Open Platform for Aggressive Autonomous Driving (2019, background): “The factors in the graph correspond to GPS measurements and preintegrated IMU measurements [36].”
On-Manifold Preintegration for Real-Time Visual--Inertial Odometry (2015, unknown): “This paper is an extension of our previous work [6] with additional experiments, an in-depth discussion of related work, and comprehensive technical derivations.”
PL-SVO: Semi-direct Monocular Visual Odometry by combining points and line segments (2016, methodology): “Solutions for the VO problem has been addressed employing different sensors, such as monocular or stereo cameras [2] [3] [4], RGB-D cameras [5] [6], or a combination of any of them with an Inertial Measurement Unit (IMU) [7].”
Factor Graphs for Robot Perception (2017, background): “[67, 68] in the context of visual-inertial odometry or VIO.”
Visual-Inertial Monocular SLAM With Map Reuse (2016, methodology): “While previous approaches [15]–[17] jointly solve vision and IMU, either ignoring gyroscope or accelerometer biases, we efﬁciently compute all variables by subdividing the problem in simpler steps.”
Direct visual-inertial odometry with stereo cameras (2016, background): “Another example of energy-based approaches is presented in [9] which combines IMU measurements with direct tracking of a sparse subset of points in the image.”
Information-Theoretic Model Predictive Control: Theory and Applications to Autonomous Driving (2017, background): “To keep computational loads low and maintain high accuracy, this graph optimizes for state nodes at 10 Hz, corresponding to GPS measurements [40].”
Robust Stereo Visual Inertial Odometry for Fast Autonomous Flight (2017, methodology): “[17] propose a direct method which minimizes photometric error directly in order to exploit more information from the images.”
Probabilistic data association for semantic SLAM (2017, methodology): “3) Inertial Factors: To incorporate the accelerometer and gyroscope measurements into the pose graph, we use the method of preintegration factors detailed in [40].”
Aggressive driving with model predictive path integral control (2016, background): “In order to keep computational loads low while maintaining high accuracy, this graph only optimizes for state nodes at 10Hz, corresponding to GPS measurements [22].”
LINS: A Lidar-Inertial State Estimator for Robust and Efficient Navigation (2019, background): “[20] presented LIPS which leveraged graph optimization over the inertial pre-integration constraints [21] and plane constraints from a lidar.”
VINet: Visual-Inertial Odometry as a Sequence-to-Sequence Learning Problem (2017, methodology): “However, the (Forster et al. 2015) system relies on iSAM2 (Kaess et al. 2011) as the back-end optimization and SVO as the front-end tracking system which we found fails more often than OK-VIS.”
Kimera: From SLAM to spatial perception with 3D dynamic scene graphs (2021, methodology): “Table 1 compares the Root Mean Squared Error (RMSE) of the Absolute Translation Error (ATE) of KimeraVIO against state-of-the-art open-source VIO pipelines: OKVIS (Leutenegger et al. 2013), MSCKF (Mourikis and Roumeliotis 2007), ROVIO (Bloesch et al. 2015), VINSMono (Qin et al. 2018), and SVO-GTSAM ”
Legged Robot State-Estimation Through Combined Forward Kinematic and Preintegrated Contact Factors (2017, methodology): “The following first order approximations of above maps allow for relating increments in the Lie Algebra to increments in the Lie group and vice versa [19]”