Non-uniform sampling strategies for continuous correction based trajectory estimation

Dubé, Renaud; Sommer, Hannes; Gawel, Abel; Bosse, Michael; Siegwart, Roland

doi:10.1109/icra.2016.7487683

Cited by 10 publications

(9 citation statements)

References 10 publications

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“…Bosse et al [21] extended the continuous 3D scan-matching formulation from [19] to a large-scale SLAM application. Later, Anderson et al [4] and Dubé et al [67] proposed more efficient implementations by using wavelets or sampling non-uniform knots over the trajectory, respectively. Tong et al [243] changed the parametrization of the trajectory from basis curves to a Gaussian process representation, where nodes in the factor graph are actual robot poses and any other pose can be interpolated by computing the posterior mean at the given time.…”

Section: Long-term Autonomy Ii: Scalabilitymentioning

confidence: 99%

Past, Present, and Future of Simultaneous Localization and Mapping: Toward the Robust-Perception Age

Cadena¹,

Carlone²,

Carrillo³

et al. 2016

IEEE Trans. Robot.

3,117

1,742

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Abstract-Simultaneous localization and mapping (SLAM) consists in the concurrent construction of a model of the environment (the map), and the estimation of the state of the robot moving within it. The SLAM community has made astonishing progress over the last 30 years, enabling large-scale real-world applications and witnessing a steady transition of this technology to industry. We survey the current state of SLAM and consider future directions. We start by presenting what is now the de-facto standard formulation for SLAM. We then review related work, covering a broad set of topics including robustness and scalability in long-term mapping, metric and semantic representations for mapping, theoretical performance guarantees, active SLAM and exploration, and other new frontiers. This paper simultaneously serves as a position paper and tutorial to those who are users of SLAM. By looking at the published research with a critical eye, we delineate open challenges and new research issues, that still deserve careful scientific investigation. The paper also contains the authors' take on two questions that often animate discussions during robotics conferences: Do robots need SLAM? and Is SLAM solved?

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Section: Long-term Autonomy Ii: Scalabilitymentioning

confidence: 99%

Past, Present, and Future of Simultaneous Localization and Mapping: Toward the Robust-Perception Age

Cadena¹,

Carlone²,

Carrillo³

et al. 2016

IEEE Trans. Robot.

3,117

1,742

View full text Add to dashboard Cite

show abstract

“…On this sequence, our real-time algorithm successfully detected 12 true positive and no false positive loop-closures. Once loops are detected, they are fed in a pose-graph optimization system similar to the one described in [29] 2 . The result of this optimization is used to update the target segment positions and remove duplicate segments from the target map as aforementioned.…”

Section: E Loop-closure Performancementioning

confidence: 99%

SegMatch: Segment based place recognition in 3D point clouds

Dubé

Dugas

Stumm

et al. 2017

2017 IEEE International Conference on Robotics and Automation (ICRA)

Self Cite

340

218

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Place recognition in 3D data is a challenging task that has been commonly approached by adapting imagebased solutions. Methods based on local features suffer from ambiguity and from robustness to environment changes while methods based on global features are viewpoint dependent. We propose SegMatch, a reliable place recognition algorithm based on the matching of 3D segments. Segments provide a good compromise between local and global descriptions, incorporating their strengths while reducing their individual drawbacks. SegMatch does not rely on assumptions of 'perfect segmentation', or on the existence of 'objects' in the environment, which allows for reliable execution on large scale, unstructured environments. We quantitatively demonstrate that SegMatch can achieve accurate localization at a frequency of 1Hz on the largest sequence of the KITTI odometry dataset. We furthermore show how this algorithm can reliably detect and close loops in real-time, during online operation. In addition, the source code for the SegMatch algorithm is made publicly available 1 .

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“…To promote the system performance, researchers proposed various sliding window algorithms, including fixed sliding window, bounded variable sliding window, adjustable sliding window, etc. [4,42,44]. These works benefit many real-time continuous location-based services.…”

Section: Online Trajectory Estimationmentioning

confidence: 99%

Online Trajectory Estimation Based on a Network-Wide Cellular Fingerprint Map

Chen

et al. 2022

Sensors

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Cellular signaling data is widely available in mobile communications and contains abundant movement sensing information of individual travelers. Using cellular signaling data to estimate the trajectories of mobile users can benefit many location-based applications, including infectious disease tracing and screening, network flow sensing, traffic scheduling, etc. However, conventional methods rely too much on heuristic hypotheses or hardware-dependent network fingerprinting approaches. To address the above issues, NF-Track (Network-wide Fingerprinting based Tracking) is proposed to realize accurate online map-matching of cellular location sequences. In particular, neither prior assumptions such as arterial preference and less-turn preference or extra hardware-relevant parameters such as RSS and SNR are required for the proposed framework. Therefore, it has a strong generalization ability to be flexibly deployed in the cloud computing environment of telecom operators. In this architecture, a novel segment-granularity fingerprint map is put forward to provide sufficient prior knowledge. Then, a real-time trajectory estimation process is developed for precise positioning and tracking. In our experiments implemented on the urban road network, NF-Track can achieve a recall rate of 91.68% and a precision rate of 90.35% in sophisticated traffic scenes, which are superior to the state-of-the-art model-based unsupervised learning approaches.

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Non-uniform sampling strategies for continuous correction based trajectory estimation

Cited by 10 publications

References 10 publications

Past, Present, and Future of Simultaneous Localization and Mapping: Toward the Robust-Perception Age

Past, Present, and Future of Simultaneous Localization and Mapping: Toward the Robust-Perception Age

SegMatch: Segment based place recognition in 3D point clouds

Online Trajectory Estimation Based on a Network-Wide Cellular Fingerprint Map

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