A Counter Example to the Theory of Simulataneous Localization and Map Building

By Simon Justin Julier, Jeffrey K. Uhlmann. IEEE International Conference on Robotics and Automation, 2001

374 citations recorded by the source corpus.

Subfields: Aerospace Engineering, Artificial Intelligence, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/c450a41574163ef04b3bf23ec2b54a854c9f7389

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Citations in the corpus

Computer Vision for Autonomous Vehicles: Problems, Datasets and State-of-the-Art (2017, background): “However, it is known that filtering always produces an inconsistent map when applied on nonlinear SLAM problems (Julier & Uhlmann (2001)), which is usually the case when dealing with real data.”
iSAM: Incremental Smoothing and Mapping (2008, background): “consistent in nonlinear SLAM settings [29], and much later work [30], [31] focused on reducing the effect of nonlinearities and providing more efficient but typically approximate solutions”
Incremental smoothing and mapping (2008, background): “Meanwhile it has been shown by Julier and Uhlmann [75] that filtering is inconsistent in nonlinear SLAM settings and much later work, such as [66, 85], focuses on reducing the effect of nonlinearities and providing more efficient, but typically approximate solutions to deal with larger environments.”
Iterated extended Kalman filter based visual-inertial odometry using direct photometric feedback (2017, background): “A key issue is to improve the consistency of the estimation framework that is affected by its inherent nonlinearity (Julier and Uhlmann (2001); Castellanos et al. (2004)).”
State Estimation for Legged Robots - Consistent Fusion of Leg Kinematics and IMU (2012, background): “The above phenomenon has been observed earlier in the context of EKF-SLAM [9, 12].”
Convergence and Consistency Analysis for Extended Kalman Filter Based SLAM (2007, methodology): “The common idea used in this paper and [ 7 ][8][11] is that the consistency of SLAM estimate is evaluated based on the fact “Keep observing new landmarks does not help in reducing the robot pose uncertainty”.”
Indoor Tracking: Theory, Methods, and Technologies (2015, methodology): “For example, the solutions based on the EKF typically suffer from linearization errors [71], [72], whereas the solutions based on the PF suffer from particle depletion [73].”
iSAM2: Incremental smoothing and mapping using the Bayes tree (2012, background): “However, ﬁlter-ing is applied, which is known to be inconsistent when applied to the inherently nonlinear SLAM problem (Julier and Uhlmann, 2001), i.e., the average taken over a large number of experiments diverges from the true solution.”
Simultaneous Localization and Mapping: A Survey of Current Trends in Autonomous Driving (2017, neutral): “in [16] add that the ignored correlation between”
Simultaneous Localisation and Mapping ( SLAM ) : Part I The Essential Algorithms (2006, background): “Non-linearity can be a significant problem in EKF-SLAM and leads to inevitable, and sometimes dramatic, inconsistency in solutions [24].”
1-Point RANSAC for extended Kalman filtering: Application to real-time structure from motion and visual odometry (2010, result): “The papers (Julier and Uhlmann, 2001; Martinelli et al., 2005) indicate that due to errors introduced in linearization EKF methods might provide inconsistent results.”
Consistency of the EKF-SLAM Algorithm (2006, methodology): “Previous investigations of EKF-SLAM consistency claim that the algorithm’s failure is time dependent [10], [4].”
Visual SLAM: Why filter? (2012, neutral): “In particular, the fact that the EKF led to inconsistencies due to linearisation issues has been studied well in the past [24].”
Simultaneous localization and mapping: part I (2006, background): “Nonlinearity can be a significant problem in EKF-SLAM and leads to inevitable, and sometimes dramatic, inconsistency in solutions [24].”
Factor Graphs for Robot Perception (2017, background): “Many attempts were made to extending the filtering approach to cope with larger-scale environments [26, 52, 130, 91, 184, 188, 92], but filtering itself was shown to be inconsistent [105] when applied to the inherently nonlinear SLAM problem.”
Robust visual inertial odometry using a direct EKF-based approach (2015, background): “A key issue is to improve the consistency of the estimation framework which is affected by its inherent nonlinearity [13], [3].”