Efficient Sparse Pose Adjustment for 2D mapping
By Kurt Konolige, Giorgio Grisetti, Rainer Kümmerle, Wolfram Burgard, Benson Limketkai, Régis Vincent. 2010 IEEE/RSJ International Conference on Intelligent Robots and Systems, 2010
425 citations recorded by the source corpus.
Subfields: Aerospace Engineering, Computer Vision and Pattern Recognition
View Paper: https://www.semanticscholar.org/paper/27bb3145c6973e52fbb5f08472c5dc8e11edd026
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Aerospace Engineering
- Michael Kaess
- Luca Carlone
- Frank Dellaert
- Andrew J. Davison
- Wolfram Burgard
- Hauke Strasdat
- Giorgio Grisetti
- John J. Leonard
In Computer Vision and Pattern Recognition
- Frank Dellaert
- Luca Carlone
- Michael Kaess
- Andrew J. Davison
- Wolfram Burgard
- Daniel Cremers
- Hauke Strasdat
- John J. Leonard
Citations in the corpus
- On behalf of: Multimedia Archives (2012, positive): “More classical nonlinear least-squares methods, such as Gauss–Newton and LM, were used by Dellaert (2005), Hertzberg (2008), Grisetti et al. (2010), Konolige et al. (2010), and Kümmerle et al. (2011).”
- Real-time loop closure in 2D LIDAR SLAM (2016, methodology): “Two common approaches for addressing the remaining local error accumulation are particle filter and graph-based SLAM [2], [8].”
- A Review of Visual-LiDAR Fusion based Simultaneous Localization and Mapping (2020, background): “Of course, adapting such techniques to 3D is very difficult due to the size of the occupancy grid.”
- A Survey on Active Simultaneous Localization and Mapping: State of the Art and New Frontiers (2022, methodology): “[59] Karto [60] & g2o [55] Lidar OG map DRL Sampled from the OG map Map’s MI & distance Sim.”
- iSAM2: Incremental smoothing and mapping using the Bayes tree (2012, methodology): “The results show that a batch solution using sparse matrix factorization (SPA, SAM) quickly gets expensive, emphasizing the need for incremental solutions. iSAM1 performs very well on sparse datasets, such as Manhattan, Killian Court and City20000, while performance degrades on datasets with denser ”
- A flexible and scalable SLAM system with full 3D motion estimation (2011, methodology): “The approach presented in this paper serves as a SLAM frontend and does not provide pose graph optimization like the solutions presented in [4] and [5].”
- G2o: A general framework for graph optimization (2011, background): “Typical instances are simultaneous localization and mapping (SLAM) [19], [5], [22], [10], [16], [26] or bundle adjustment (BA) [27], [15], [18].”