Spatio-temporal representation for long-term anticipation of human presence in service robotics

Vintr, Tomáš; Yan, Zhi; Duckett, Tom; Krajník, Tomáš

doi:10.1109/icra.2019.8793534

Cited by 23 publications

(37 citation statements)

References 24 publications

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“…Daily routines in public environments could be exploited by a service robot, for example, to collect negative background samples at night, when there are no moving objects, and positive human sam-ples during the day 9 . Long-term operation and open-ended learning are therefore two promising directions for future research in this area [45]. Future work should look at other classification methods such as deep neural networks, exploiting online learning to overcome the difficulty of collecting extensive training samples.…”

Section: Discussionmentioning

confidence: 99%

Online learning for 3D LiDAR-based human detection: experimental analysis of point cloud clustering and classification methods

2019

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This paper presents a system for online learning of human classifiers by mobile service robots using 3D Li-DAR sensors, and its experimental evaluation in a large indoor public space. The learning framework requires a minimal set of labelled samples (e.g. one or several samples) to initialise a classifier. The classifier is then retrained iteratively during operation of the robot. New training samples are generated automatically using multi-target tracking and a pair of "experts" to estimate false negatives and false positives. Both classification and tracking utilise an efficient real-time clustering algorithm for segmentation of 3D point cloud data. We also introduce a new feature to improve human classification in sparse, long-range point clouds. We provide an extensive evaluation of our the framework using a 3D LiDAR dataset of people moving in a large indoor public space, which is made available to the research community. The experiments demonstrate the influence of the system components and improved classification of humans compared to the state-of-the-art.

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Section: Discussionmentioning

confidence: 99%

Online learning for 3D LiDAR-based human detection: experimental analysis of point cloud clustering and classification methods

2019

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“…This is caused by the fact that the modeled events are sparse, and the process generating them is not stationary. To deal with the problem, we proposed in our previous works to we use a "warped-hypertime" projection of the time line into a closed subset of multidimensional vector space, where each pair of dimensions would represent one periodicity [38], [39], [40], [41], [42]. Then, we create a model characterising the probability distribution of spatiodirection-temporal events in the vector space extended by the warped hypertime.…”

Section: Methods Descriptionmentioning

confidence: 99%

“…where n is the number of positions, k is the number of angular bins for the direction of people motion in the cells C. Methods compared in the experiment 1) WHyTe: There are two parameters, which affect the quality of WHyTe -the number of clusters c and the set of periodicities. The recent experiments showed, that the number of clusters could be relatively small (usually up to 9) [42], and it seems, that the number of clusters is in relation with the topological structure of the space [41]. For this dataset from T-junction we chose c = 3 clusters.…”

Section: B Evaluation Methodologymentioning

confidence: 99%

“…For this dataset from T-junction we chose c = 3 clusters. The second parameters can be derived from data iteratively, but recent experiments showed [42], [41], that the quality of prediction do not usualy grow with more than 3 added hypertime circles. We selected the basic set of periodicities as proposed in [7], and found out, that there were two strongly prominent components in the training data, which we used in our method.…”

Section: B Evaluation Methodologymentioning

confidence: 99%

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Time-varying Pedestrian Flow Models for Service Robots

Vintr¹,

Molina

Senanayake

et al. 2019

2019 European Conference on Mobile Robots (ECMR)

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We present a human-centric spatio-temporal model for service robots operating in densely populated environments for long time periods. The method integrates observations of pedestrians performed by a mobile robot at different locations and times into a memory efficient model, that represents the spatial layout of natural pedestrian flows and how they change over time. To represent temporal variations of the observed flows, our method does not model the time in a linear fashion, but by several dimensions wrapped into themselves. This representation of time can capture long-term (i.e. days to weeks) periodic patterns of peoples' routines and habits. Knowledge of these patterns allows making long-term predictions of future human presence and walking directions, which can support mobile robot navigation in human-populated environments. Using datasets gathered by a robot for several weeks, we compare the model to state-of-the-art methods for pedestrian flow modelling.

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“…point cloud) about its environment over a long range and wide angle. Moreover, it is robust to lightness variance, thereby very suitable for long-term robot autonomy (Krajník et al, 2019;Vintr et al, 2019;Kunze et al, 2018). However, due to the low feature density compared to cameras, false positives are more likely.…”

Section: Human Detection and Trackingmentioning

confidence: 99%

Robot perception of static and dynamic objects with an autonomous floor scrubber

Yan

Schreiberhuber

Halmetschlager

et al. 2020

Intel Serv Robotics

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This paper presents the perception system of a new professional cleaning robot for large public places. The proposed system is based on multiple sensors including 3D and 2D lidar, two RGB-D cameras and a stereo camera. The two lidars together with an RGB-D camera are used for dynamic object (human) detection and tracking, while the second RGB-D and stereo camera are used for detection of static objects (dirt and ground objects). A learning and reasoning module for spatial-temporal representation of the environment based on the perception pipeline is also introduced. Furthermore, a new dataset collected with the robot in several public places, including a supermarket, a warehouse and an airport, is released. Baseline results on this dataset for further research and comparison are provided. The proposed system has been fully implemented into the Robot Operating System (ROS) with high modularity, also publicly available to the community. Keywords Robot perception • Human detection and tracking • Object and dirt detection • Spatial-temporal representation • Dataset • ROS This work has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 645376 (FLOBOT).

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Spatio-temporal representation for long-term anticipation of human presence in service robotics

Cited by 23 publications

References 24 publications

Online learning for 3D LiDAR-based human detection: experimental analysis of point cloud clustering and classification methods

Online learning for 3D LiDAR-based human detection: experimental analysis of point cloud clustering and classification methods

Time-varying Pedestrian Flow Models for Service Robots

Robot perception of static and dynamic objects with an autonomous floor scrubber

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