A Semantic Segmentation Based Lidar SLAM System Towards Dynamic Environments

Jian, Rui; Su, Weihua; Li, Ruihao; Zhang, Shiyue; Wei, Jiacheng; Li, Boyang; Huang, Ruqiang

doi:10.1007/978-3-030-27535-8_52

Cited by 11 publications

(8 citation statements)

References 15 publications

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“…4 shows a few examples of 3D-MiniNet inference on test data. The supplementary video includes inference results on a full sequence 1 . As test ground-truth is not provided for the test set (evaluation is performed externally on the online platform), we can only show visual results with no label comparison.…”

Section: B Benchmarks Resultsmentioning

confidence: 99%

“…This detailed semantic information is essential for decision making in real-world dynamic scenarios. LIDAR semantic segmentation provides very useful information to autonomous robots when performing tasks such as Simultaneous Localization And Mapping (SLAM) [1], [2], autonomous driving [3] or inventory tasks [4], especially for identifying dynamic objects. In these scenarios, it is critical to have models that provide accurate semantic information in a fast and efficient manner, which is particularly challenging working with 3D LIDAR data.…”

Section: Introductionmentioning

confidence: 99%

“…On the other hand, approaches that project the 3D information into a 2D image (projectionbased approaches) are more efficient but typically less accurate [3], [8], [9] because they do not exploit the raw 3D information. Recent results on fast [3] and parameterefficient [10] semantic segmentation models are facilitating 1 RoPeRt group, at DIIS -I3A, Universidad de Zaragoza, Spain.…”

Section: Introductionmentioning

confidence: 99%

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3D-MiniNet: Learning a 2D Representation from Point Clouds for Fast and Efficient 3D LIDAR Semantic Segmentation

Alonso¹,

Riazuelo²,

Montesano³

et al. 2020

Preprint

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LIDAR semantic segmentation, which assigns a semantic label to each 3D point measured by the LIDAR, is becoming an essential task for many robotic applications such as autonomous driving. Fast and efficient semantic segmentation methods are needed to match the strong computational and temporal restrictions of many of these real-world applications.This work presents 3D-MiniNet, a novel approach for LIDAR semantic segmentation that combines 3D and 2D learning layers. It first learns a 2D representation from the raw points through a novel projection which extracts local and global information from the 3D data. This representation is fed to an efficient 2D Fully Convolutional Neural Network (FCNN) that produces a 2D semantic segmentation. These 2D semantic labels are re-projected back to the 3D space and enhanced through a post-processing module. The main novelty in our strategy relies on the projection learning module. Our detailed ablation study shows how each component contributes to the final performance of 3D-MiniNet. We validate our approach on well known public benchmarks (SemanticKITTI and KITTI), where 3D-MiniNet gets state-of-the-art results while being faster and more parameter-efficient than previous methods.

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Section: B Benchmarks Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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3D-MiniNet: Learning a 2D Representation from Point Clouds for Fast and Efficient 3D LIDAR Semantic Segmentation

Alonso¹,

Riazuelo²,

Montesano³

et al. 2020

Preprint

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“…Reference [19] develops a laser-inertial odometry and mapping method which consists of four sequential modules to perform a real-time and robust pose estimation for large scale high-way environments. Reference [20] presents a dynamic objects-free LOAM system by overlapping segmented images into LiDAR scans. Although deep learning-based methods can effectively alleviate the impact of dynamic objects on the SLAM performance, they are normally difficult to operate in real-time due to the implementation of deeplearning neural networks which possess high computational complexity.…”

Section: B Deep Learning-based Dynamic Slammentioning

confidence: 99%

Multi-modal Semantic SLAM for Complex Dynamic Environments

Wang¹,

Ko²,

Xie³

2022

Preprint

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Simultaneous Localization and Mapping (SLAM) is one of the most essential techniques in many real-world robotic applications. The assumption of static environments is common in most SLAM algorithms, which however, is not the case for most applications. Recent work on semantic SLAM aims to understand the objects in an environment and distinguish dynamic information from a scene context by performing image-based segmentation. However, the segmentation results are often imperfect or incomplete, which can subsequently reduce the quality of mapping and the accuracy of localization. In this paper, we present a robust multi-modal semantic framework to solve the SLAM problem in complex and highly dynamic environments. We propose to learn a more powerful object feature representation and deploy the mechanism of looking and thinking twice to the backbone network, which leads to a better recognition result to our baseline instance segmentation model. Moreover, both geometric-only clustering and visual semantic information are combined to reduce the effect of segmentation error due to small-scale objects, occlusion and motion blur. Thorough experiments have been conducted to evaluate the performance of the proposed method. The results show that our method can precisely identify dynamic objects under recognition imperfection and motion blur. Moreover, the proposed SLAM framework is able to efficiently build a static dense map at a processing rate of more than 10 Hz, which can be implemented in many practical applications. Both training data and the proposed method is open sourced 1 .

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“…Ref. [ 35 ] proposed a dynamic objects-free LOAM system with overlapping the segmented images into LiDAR scans. In order to achieve the same purpose, Han et al combined ORB-SLAM2 and PSPNet segmentation [ 36 ].…”

Section: Related Workmentioning

confidence: 99%

Semantic Point Cloud Mapping of LiDAR Based on Probabilistic Uncertainty Modeling for Autonomous Driving

Cho

Kim

Park

et al. 2020

Sensors

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LiDAR-based Simultaneous Localization And Mapping (SLAM), which provides environmental information for autonomous vehicles by map building, is a major challenge for autonomous driving. In addition, the semantic information has been used for the LiDAR-based SLAM with the advent of deep neural network-based semantic segmentation algorithms. The semantic segmented point clouds provide a much greater range of functionality for autonomous vehicles than geometry alone, which can play an important role in the mapping step. However, due to the uncertainty of the semantic segmentation algorithms, the semantic segmented point clouds have limitations in being directly used for SLAM. In order to solve the limitations, this paper proposes a semantic segmentation-based LiDAR SLAM system considering the uncertainty of the semantic segmentation algorithms. The uncertainty is explicitly modeled by proposed probability models which are come from the data-driven approaches. Based on the probability models, this paper proposes semantic registration which calculates the transformation relationship of consecutive point clouds using semantic information with proposed probability models. Furthermore, the proposed probability models are used to determine the semantic class of the points when the multiple scans indicate different classes due to the uncertainty. The proposed framework is verified and evaluated by the KITTI dataset and outdoor environments. The experiment results show that the proposed semantic mapping framework reduces the errors of the mapping poses and eliminates the ambiguity of the semantic information of the generated semantic map.

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A Semantic Segmentation Based Lidar SLAM System Towards Dynamic Environments

Cited by 11 publications

References 15 publications

3D-MiniNet: Learning a 2D Representation from Point Clouds for Fast and Efficient 3D LIDAR Semantic Segmentation

3D-MiniNet: Learning a 2D Representation from Point Clouds for Fast and Efficient 3D LIDAR Semantic Segmentation

Multi-modal Semantic SLAM for Complex Dynamic Environments

Semantic Point Cloud Mapping of LiDAR Based on Probabilistic Uncertainty Modeling for Autonomous Driving

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