Normal distributions transform Monte-Carlo localization (NDT-MCL)

Saarinen, Jyrki; Andreasson, Henrik; Stoyanov, Todor; Lilienthal, Achim J.

doi:10.1109/iros.2013.6696380

Cited by 91 publications

(55 citation statements)

References 14 publications

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“…In severe cases this results in wrong associations between map and observation, which causes the pose distribution to diverge. On the other hand, in [1] it was shown that the maximum aposteriori estimate of NDT-MCL is very accurate in static environments. The accuracy also decreased minimally in the presence of dynamic objects (e.g.…”

Section: B Dual-timescale Ndt-mclmentioning

confidence: 99%

“…Normal Distribution Transform Monte-Carlo Localization (NDT-MCL) was introduced in [1]. It employs NDT [2,3] to represent both, the map and the measurements.…”

Section: A Ndt-mclmentioning

confidence: 99%

“…We build on our previous work in [1]: Normal Distributions Transform Monte-Carlo Localization (NDT-MCL). It employs the Normal Distributions Transforms (NDT) representation for an MCL implementation.…”

Section: Introductionmentioning

confidence: 99%

“…Moreover, NDT is by nature a likelihood model, which makes it an ideal candidate to be utilized in MCL. In [1] it was shown that NDT-MCL provides superior performance over the occupancy grid-MCL, where occupancy grids cannot represent the environment accurately, especially in presence of sensor noise. Moreover, NDT-MCL showed potential to meet the industrial requirements for both accuracy and repeatability.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Localization in highly dynamic environments using dual-timescale NDT-MCL

Valencia

Saarinen

Andreasson

et al. 2014

2014 IEEE International Conference on Robotics and Automation (ICRA)

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Abstract-Industrial environments are rarely static and often their configuration is continuously changing due to the material transfer flow. This is a major challenge for infrastructure free localization systems. In this paper we address this challenge by introducing a localization approach that uses a dualtimescale approach. The proposed approach -Dual-Timescale Normal Distributions Transform Monte Carlo Localization (DT-NDT-MCL) -is a particle filter based localization method, which simultaneously keeps track of the pose using an apriori known static map and a short-term map. The short-term map is continuously updated and uses Normal Distributions Transform Occupancy maps to maintain the current state of the environment. A key novelty of this approach is that it does not have to select an entire timescale map but rather use the best timescale locally. The approach has real-time performance and is evaluated using three datasets with increasing levels of dynamics. We compare our approach against previously proposed NDT-MCL and commonly used SLAM algorithms and show that DT-NDT-MCL outperforms competing algorithms with regards to accuracy in all three test cases.

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Section: B Dual-timescale Ndt-mclmentioning

confidence: 99%

“…Normal Distribution Transform Monte-Carlo Localization (NDT-MCL) was introduced in [1]. It employs NDT [2,3] to represent both, the map and the measurements.…”

Section: A Ndt-mclmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Localization in highly dynamic environments using dual-timescale NDT-MCL

Valencia

Saarinen

Andreasson

et al. 2014

2014 IEEE International Conference on Robotics and Automation (ICRA)

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“…A 3D range sensor (Velodyne HDL-32E) is used for mapping and localization. Mapping is carried out with the NDTFusion algorithm [12], while NDT-MCL [13] is used for the localization. First, an exploration plan for gas detection was generated using the conv-SPP algorithm in [2].…”

Section: Real World Experimentsmentioning

confidence: 99%

The right direction to smell: Efficient sensor planning strategies for robot assisted gas tomography

Arain

Schaffernicht

Bennetts

et al. 2016

2016 IEEE International Conference on Robotics and Automation (ICRA)

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Abstract-Creating an accurate model of gas emissions is an important task in monitoring and surveillance applications. A promising solution for a range of real-world applications are gas-sensitive mobile robots with spectroscopy-based remote sensors that are used to create a tomographic reconstruction of the gas distribution. The quality of these reconstructions depends crucially on the chosen sensing geometry. In this paper we address the problem of sensor planning by investigating sensing geometries that minimize reconstruction errors, and then formulate an optimization algorithm that chooses sensing configurations accordingly. The algorithm decouples sensor planning for single high concentration regions (hotspots) and subsequently fuses the individual solutions to a global solution consisting of sensing poses and the shortest path between them. The proposed algorithm compares favorably to a template matching technique in a simple simulation and in a real-world experiment. In the latter, we also compare the proposed sensor planning strategy to the sensing strategy of a human expert and find indications that the quality of the reconstructed map is higher with the proposed algorithm.

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Normal Distributions Transform Traversability Maps: LIDAR‐Only Approach for Traversability Mapping in Outdoor Environments

Ahtiainen

Stoyanov

Saarinen³

2016

Journal of Field Robotics

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Safe and reliable autonomous navigation in unstructured environments remains a challenge for field robots. In particular, operating on vegetated terrain is problematic, because simple purely geometric traversability analysis methods typically classify dense foliage as nontraversable. As traversing through vegetated terrain is often possible and even preferable in some cases (e.g., to avoid executing longer paths), more complex multimodal traversability analysis methods are necessary. In this article, we propose a three‐dimensional (3D) traversability mapping algorithm for outdoor environments, able to classify sparsely vegetated areas as traversable, without compromising accuracy on other terrain types. The proposed normal distributions transform traversability mapping (NDT‐TM) representation exploits 3D LIDAR sensor data to incrementally expand normal distributions transform occupancy (NDT‐OM) maps. In addition to geometrical information, we propose to augment the NDT‐OM representation with statistical data of the permeability and reflectivity of each cell. Using these additional features, we train a support‐vector machine classifier to discriminate between traversable and nondrivable areas of the NDT‐TM maps. We evaluate classifier performance on a set of challenging outdoor environments and note improvements over previous purely geometrical traversability analysis approaches.

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Normal distributions transform Monte-Carlo localization (NDT-MCL)

Cited by 91 publications

References 14 publications

Localization in highly dynamic environments using dual-timescale NDT-MCL

Localization in highly dynamic environments using dual-timescale NDT-MCL

The right direction to smell: Efficient sensor planning strategies for robot assisted gas tomography

Normal Distributions Transform Traversability Maps: LIDAR‐Only Approach for Traversability Mapping in Outdoor Environments

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