SuMa++: Efficient LiDAR-based Semantic SLAM

Chen, Xieyuanli; Milioto, Andres; Palazzolo, Emanuele; Giguère, Philippe; Behley, Jens; Stachniss, Cyrill

doi:10.1109/iros40897.2019.8967704

Cited by 378 publications

(222 citation statements)

References 39 publications

(51 reference statements)

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“…The mapping trajectory is compared with two types of previous approaches; Basic LiDAR SLAM without semantic information, Semantic mapping without the explicit model of uncertainty. More specifically, LOAM [ 16 ] is adopted for the basic LiDAR SLAM, and SuMa++ [ 7 ] is selected for the comparison with the semantic mapping only when the RangeNet++ is used for the semantic segmentation algorithm. Figure 16 shows the trajectory evaluation results using KITTI datasets.…”

Section: Methodsmentioning

confidence: 99%

“…In [ 39 ], Semantic LOAM (SLOAM) was proposed for forest inventory using an end-to-end pipeline for tree diameter estimation. Furthermore, SuMa++ presented semantic ICP and dynamic filtering using the semantic information from RangeNet++ for semantic mapping [ 7 ]. These studies showed the technological breakthrough in the semantic mapping field, however, the uncertainty from the semantic segmentation algorithms was not explicitly considered.…”

Section: Related Workmentioning

confidence: 99%

“…However, in real driving environments, there are a lot of dynamic objects which cause negative effects by moving during data acquisition [ 6 ]. In this situation, the semantic segmentation methods help to distinguish the dynamic objects because they provide the class of each point such as a car, pedestrian, and so on [ 7 ]. Another advantage of the semantic segmentation for the SLAM is that it can make the corresponding pairs for the point cloud registration.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

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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Section: Methodsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

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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“…Besides the fact that depth information is not considered, one significant difference from our approach is that this work uses 2D bounding boxes to represent the objects. Other innovative approaches have focused on point-wise semantic labeling for 3D lidar data within the SLAM framework itself [11]. This work also highlights urban scenarios for autonomous driving as an important application area.…”

Section: Related Workmentioning

confidence: 98%

VPS-SLAM: Visual Planar Semantic SLAM for Aerial Robotic Systems

et al. 2020

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Indoor environments have abundant presence of high-level semantic information which can provide a better understanding of the environment for robots to improve the uncertainty in their pose estimate. Although semantic information has proved to be useful, there are several challenges faced by the research community to accurately perceive, extract and utilize such semantic information from the environment. In order to address these challenges, in this paper we present a lightweight and real-time visual semantic SLAM framework running on board aerial robotic platforms. This novel method combines low-level visual/visual-inertial odometry (VO/VIO) along with geometrical information corresponding to planar surfaces extracted from detected semantic objects. Extracting the planar surfaces from selected semantic objects provides enhanced robustness and makes it possible to precisely improve the metric estimates rapidly, simultaneously generalizing to several object instances irrespective of their shape and size. Our graph-based approach can integrate several state of the art VO/VIO algorithms along with the state of the art object detectors in order to estimate the complete 6DoF pose of the robot while simultaneously creating a sparse semantic map of the environment. No prior knowledge of the objects is required, which is a significant advantage over other works. We test our approach on a standard RGB-D dataset comparing its performance with the state of the art SLAM algorithms. We also perform several challenging indoor experiments validating our approach in presence of distinct environmental conditions and furthermore test it on board an aerial robot.

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“…Most semantic SLAM methods fuse semantic labels obtained from semantic segmentation and maps generated by the SLAM algorithm to generate 3D maps with semantic information. According to the type of sensor they used, semantic SLAM algorithms can be classified as the monocular camerabased [17]- [19], stereo camera-based [20], [21], LiDARbased [22]- [24], multiple sensors-based [25], [26], RGB-D camera-based approaches, and so on. This paper mainly considers semantic SLAM algorithms based on the RGB-D camera.…”

Section: Semantic Slammentioning

confidence: 99%

Semantic RGB-D SLAM for Rescue Robot Navigation

et al. 2020

Self Cite

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In this paper, we propose a semantic simultaneous localization and mapping (SLAM) framework for rescue robots, and report its use in navigation tasks. Our framework can generate not only geometric maps in the form of dense point-clouds but also corresponding point-wise semantic labels generated by a semantic segmentation convolutional neural network (CNN). The semantic segmentation CNN is trained using our RGB-D dataset of the RoboCup Rescue-Robot-League (RRL) competition environment. With the help of semantic information, the rescue robot can identify different types of terrains in a complex environment, so as to avoid specific obstacles or to choose routes with better traversability. To reduce the segmentation noise, our approach utilizes depth images to perform filtering on the segmentation results of each frame. The overall semantic map is then further improved in the point-cloud voxels. By accumulating results of multiple frames in the voxels, semantic maps with consistent semantic labels are obtained. To show the advantage of having a semantic map of the environment, we report a case study of how the semantic map can be utilized in a navigation task to reduce the arrival time while ensuring safety. The experimental result shows that our semantic SLAM framework is capable of generating a dense semantic map for the complex RRL competition environment, with which the arrival time of the navigation time is effectively reduced.

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SuMa++: Efficient LiDAR-based Semantic SLAM

Cited by 378 publications

References 39 publications

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

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

VPS-SLAM: Visual Planar Semantic SLAM for Aerial Robotic Systems

Semantic RGB-D SLAM for Rescue Robot Navigation

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