Multisensor Online Transfer Learning for 3D LiDAR-Based Human Detection with a Mobile Robot

Yan, Zhi; Sun, Li; Duckctr, Tom; Bellotto, Nicola

doi:10.1109/iros.2018.8593899

Cited by 30 publications

(30 citation statements)

References 28 publications

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“…Leveraging our instrumented car, a ROS-based dataset is cumulatively recorded and is publicly available to the community. This dataset is full of new research challenges and as it contains periodic changes, it is especially suitable for longterm autonomy research such as persistent mapping [3], longterm prediction [14], [2], and online/lifelong learning [1], [6], [13], [27], [28]. We hope our efforts and on-the-shelf experience could pursue the development and help on solving related problems in AD.…”

Section: Discussionmentioning

confidence: 99%

“…Commonly used sensors include various cameras, 2D/3D lidar (LIght Detection And Ranging), radar (RAdio Detection And Ranging), IMU (Inertial Measurement Unit), and GNSS (Global Navigation Satellite System). The combination use of these is mainly due to the fact that different sensors have different (physical) properties, and each category has its own pros and cons [6]. On the other hand, ROS (Robot Operating System) [7] has become the de facto standard platform for development of software in robotics, and today increasing numbers of researchers and industries develop autonomous vehicles software based on it.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

EU Long-term Dataset with Multiple Sensors for Autonomous Driving

Yan

Sun

Krajník

et al. 2020

2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

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The field of autonomous driving has grown tremendously over the past few years, along with the rapid progress in sensor technology. One of the major purposes of using sensors is to provide environment perception for vehicle understanding, learning and reasoning, and ultimately interacting with the environment. In this paper, we first introduce a multisensor platform allowing vehicle to perceive its surroundings and locate itself in a more efficient and accurate way. The platform integrates eleven heterogeneous sensors including various cameras and lidars, a radar, an IMU (Inertial Measurement Unit), and a GPS-RTK (Global Positioning System / Real-Time Kinematic), while exploits a ROS (Robot Operating System) based software to process the sensory data. Then, we present a new dataset (https://epan-utbm.github.io/ utbm_robocar_dataset/) for autonomous driving captured many new research challenges (e.g. highly dynamic environment), and especially for long-term autonomy (e.g. creating and maintaining maps), collected with our instrumented vehicle, publicly available to the community.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

EU Long-term Dataset with Multiple Sensors for Autonomous Driving

Yan

Sun

Krajník

et al. 2020

2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

Self Cite

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“…Although currently used for human detection and tracking, our software could be extended to deal with other moving objects such as cars, bicycles or animals [42]. The 3D LiDARbased cluster detection module could also be replaced by other detectors based on different sensors, such as RGB-D cameras and 2D LiDARs [48].…”

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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“…3D LiDARs : The authors in [ 29 ] detected people in the immediate environment using Online Transfer Learning, by means of CNNs that were trained using information from other sensors such as 2D LiDARs or RGB-D cameras. An online learning framework for human classification using an efficient 3D cluster detector was presented in [ 30 ].…”

Section: Perceptionmentioning

confidence: 99%

From Perception to Navigation in Environments with Persons: An Indoor Evaluation of the State of the Art

Sánchez

Ceriotti

Capitán

et al. 2022

Sensors

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Research in the field of social robotics is allowing service robots to operate in environments with people. In the aim of realizing the vision of humans and robots coexisting in the same environment, several solutions have been proposed to (1) perceive persons and objects in the immediate environment; (2) predict the movements of humans; as well as (3) plan the navigation in agreement with socially accepted rules. In this work, we discuss the different aspects related to social navigation in the context of our experience in an indoor environment. We describe state-of-the-art approaches and experiment with existing methods to analyze their performance in practice. From this study, we gather first-hand insights into the limitations of current solutions and identify possible research directions to address the open challenges. In particular, this paper focuses on topics related to perception at the hardware and application levels, including 2D and 3D sensors, geometric and mainly semantic mapping, the prediction of people trajectories (physics-, pattern- and planning-based), and social navigation (reactive and predictive) in indoor environments.

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Multisensor Online Transfer Learning for 3D LiDAR-Based Human Detection with a Mobile Robot

Cited by 30 publications

References 28 publications

EU Long-term Dataset with Multiple Sensors for Autonomous Driving

EU Long-term Dataset with Multiple Sensors for Autonomous Driving

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

From Perception to Navigation in Environments with Persons: An Indoor Evaluation of the State of the Art

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