Deep semantic classification for 3D LiDAR data
Open accessAbstract
Robots are expected to operate autonomously in dynamic environments. Understanding the underlying dynamic characteristics of objects is a key enabler for achieving this goal. In this paper, we propose a method for pointwise semantic classification of 3D LiDAR data into three classes: non-movable, movable and dynamic. We concentrate on understanding these specific semantics because they characterize important information required for an autonomous system. To learn the distinction between movable and non-movable points in the environment, we introduce an approach based on deep neural network and for detecting the dynamic points, we estimate pointwise motion. We propose a Bayes filter framework for combining the learned semantic cues with the motion cues to infer the required semantic classification. In extensive experiments, we compare our approach with other methods on a standard benchmark dataset and report competitive results in comparison to the existing state-of-the-art. Furthermore, we show an improvement in the classification of points by combining the semantic cues retrieved from the neural network with the motion cues.
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- Authors
- Dewan, A., Oliveira, G. L., & Burgard, W.
- Affiliations
- University of Freiburg (DE)
- Venue
- 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
- Publisher
- IEEE
- Published
- 2017-09
- Pages
- 3544-3549
- ISSN
- 2153-0858 (ISSN-L)
- OpenAlex
- W2705009888
- Cited by
- 60
- References
- 25
- Citations by year
- 2026: 2 · 2025: 4 · 2024: 3 · 2023: 4 · 2022: 3 · 2021: 12 · 2020: 10 · 2019: 15 · 2018: 6 · 2017: 1
- FWCI
- 15.32
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Funders
- European Commission (610532)
- Topics
- Physical Sciences › Engineering › Aerospace Engineering › Robotics and Sensor-Based Localization
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Physical Sciences › Engineering › Computational Mechanics › 3D Shape Modeling and Analysis
- Keywords
- 3D LiDAR, moving object detection, Bayesian filtering, deep neural network, autonomous robots, dynamic environments
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