Simultaneous Localization and Mapping Using a Novel Dual Quaternion Particle Filter
Open accessAbstract
In this paper, we present a novel approach to perform simultaneous localization and mapping (SLAM) for planar motions based on stochastic filtering with dual quaternion particles using low-cost range and gyro sensor data. Here, SE(2) states are represented by unit dual quaternions and further get stochastically modeled by a distribution from directional statistics such that particles can be generated by random sampling. To build the full SLAM system, a novel dual quaternion particle filter based on Rao-Blackwellization is proposed for the tracking block, which is further integrated with an occupancy grid mapping block. Unlike previously proposed filtering approaches, our method can perform tracking in the presence of multi-modal noise in unknown environments while giving reasonable mapping results. The approach is further evaluated using a walking robot with on-board ultrasonic sensors and an IMU sensor navigating in an unknown environment in both simulated and real-world scenarios.
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Dual quaternions have proven to be an efficient and useful representation for vision topics such as hand-eye calibration, simultaneous mapping and localisation, and pose estimation and tracking.
“More specifically, they have proven to be an efficient and useful representation for many topics related to vision, such as hand-eye calibration [11], simultaneous mapping and localisation [12] and pose estimation and tracking [13]-[15].”
From Dual Quaternion-Based Visual Servoing for Grasping Moving Objects (2021) · cites this paper as [12]
Cites
Thrun, S. (2002) · CACM · doi:10.1145/504729.504754 · Cited by 7,941 · see citing works
Unscented Filtering and Nonlinear Estimation
Julier, S., & Uhlmann, J. K. (2004) · Proceedings of the IEEE · doi:10.1109/jproc.2003.823141 · Cited by 6,619 · see citing works
Beyond the Kalman filter particle filters for tracking applications
Ristić, B., Arulampalam, S., & Gordon, N. (2004) · Cited by 3,400 · see citing works
Mardia, K. V., & Jupp, P. E. (1999) · Wiley series in probability and statistics · doi:10.1002/9780470316979 · Cited by 3,277 · see citing works
FastSLAM: a factored solution to the simultaneous localization and mapping problem
Montemerlo, M., Thrun, S., Koller, D., & Wegbreit, B. (2002) · doi:10.5555/777092.777184 · Cited by 2,081 · see citing works
Monte Carlo localization for mobile robots
Dellaert, F., Fox, D., Burgard, W., & Thrun, S. (2003) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/robot.1999.772544 · Cited by 1,622 · see citing works
Preliminary Sketch of Biquaternions
Clifford (1871) · Proceedings of the London Mathematical Society · doi:10.1112/plms/s1-4.1.381 · Cited by 628 · see citing works
An Antipodally Symmetric Distribution on the Sphere
Bingham, C. R. (1974) · The Annals of Statistics · doi:10.1214/aos/1176342874 · Cited by 582 · see citing works
Large-scale direct SLAM with stereo cameras
Engel, J. J., Stückler, J., & Cremers, D. (2015) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros.2015.7353631 · Cited by 566 · see citing works
II. On quaternions; or on a new system of imaginaries in algebra
Hamilton, W. (1844) · The London Edinburgh and Dublin Philosophical Magazine and Journal of Science · doi:10.1080/14786444408644923 · Cited by 188 · see citing works
Unscented Orientation Estimation Based on the Bingham DistributionPreprint
Gilitschenski, I., Kurza, G., Julier, S., et al. (2016) · Cited by 66 · see citing works
Unscented Orientation Estimation Based on the Bingham Distribution
Gilitschenski, I., Kurz, G., Julier, S. J., et al. (2015) · IEEE Transactions on Automatic Control · doi:10.1109/tac.2015.2423831 · Cited by 49 · see citing works
Self-citationTemeltaş, H., & Kayak, D. (2008) · IEEE Aerospace and Electronic Systems Magazine · doi:10.1109/maes.2008.4694832 · Cited by 46 · see citing works
On Extended Target Tracking Using PHD Filters
Granström, K., Lundquist, C., Gustafsson, F., et al. (2012) · Cited by 44 · see citing works
Progressive Gaussian Filtering Using Explicit Likelihoods
Steinbring, J., & Hanebeck, U. D. (2014) · Repository KITopen (Karlsruhe Institute of Technology) · doi:10.13140/2.1.1561.4721 · Cited by 38 · see citing works
Self-citationImage Gradient-based Joint Direct Visual Odometry for Stereo Camera
Zhu, J. (2017) · International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2017/636 · Cited by 31 · see citing works
A new probability distribution for simultaneous representation of uncertain position and orientation
Gilitschenski, I., Kurz, G., Julier, S., et al. (2014) · Repository KITopen (Karlsruhe Institute of Technology) · doi:10.5445/ir/1000044821 · Cited by 24 · see citing works
Self-citationRigid motion estimation using mixtures of projected Gaussians
Feiten, W., Lang, M., & Hirche, S. (2013) · Cited by 19 · see citing works
Nonlinear Progressive Filtering for SE(2) Estimation
Li, K., Kurz, G., Bernreiter, L., et al. (2018) · International Conference on Information Fusion (FUSION) · doi:10.23919/icif.2018.8455231 · Cited by 15 · see citing works
Self-citationA stochastic filter for planar rigid-body motions
Gilitschenski, I., Kurz, G., & Hanebeck, U. D. (2015) · IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI) · doi:10.1109/mfi.2015.7295738 · Cited by 9 · see citing works
Self-citationMPG - Fast Forward Reasoning on 6 DOF Pose Uncertainty
Lang, M., & Feiten, W. (2011) · mediaTUM – the media and publications repository of the Technical University Munich (Technical University Munich) · Cited by 8 · see citing works
Darling, J. E., & DeMars, K. J. (2016) · Cited by 8 · see citing works
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Dual Quaternion-Based Visual Servoing for Grasping Moving Objects
De Farias, C., Adjigble, M., Tamadazte, B., Stolkin, R., & Marturi, N. (2021) · 2021 IEEE 17th International Conference on Automation Science and Engineering (CASE) · doi:10.1109/case49439.2021.9551631 · Cited by 1 · see citing works
“More specifically, they have proven to be an efficient and useful representation for many topics related to vision, such as hand-eye calibration [11], simultaneous mapping and localisation [12] and pose estimation and tracking [13]–[15].”
Background
- Authors
- Li, K., Kurz, G., Bernreiter, L., & Hanebeck, U. D.
- Affiliations
- Karlsruhe Institute of Technology (DE)
- Venue
- 2018 21st International Conference on Information Fusion (FUSION)
- Publisher
- IEEE
- Published
- 2018-07
- Pages
- 1668-1675
- OpenAlex
- W2889902238
- Cited by
- 13
- References
- 22
- Citations by year
- 2026: 1 · 2025: 1 · 2021: 2 · 2020: 4 · 2019: 4 · 2018: 1
- FWCI
- 4.78
- Type
- conference paper
- Language
- en
- Open access
- Locations
- International Conference on Information Fusion (FUSION) (publishedVersion)
- Repository KITopen (Karlsruhe Institute of Technology) (submittedVersion)
- KITopen (other-oa)
- Funders
- Deutsche Forschungsgemeinschaft (HA 3789/16-1)
- Topics
- Physical Sciences › Engineering › Aerospace Engineering › Robotics and Sensor-Based Localization
- Physical Sciences › Computer Science › Artificial Intelligence › Target Tracking and Data Fusion in Sensor Networks
- Physical Sciences › Engineering › Aerospace Engineering › Inertial Sensor and Navigation
- Keywords
- SLAM, dual quaternions, particle filter, Rao-Blackwellization, circular statistics, occupancy grid mapping, ultrasonic sensors, inertial measurement unit, walking robot, planar motion
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