Real-time 3D Mobile Mapping for the Built Environment

Chen, Jingdao; Cho, Yong

doi:10.22260/isarc2016/0028

Cited by 9 publications

(4 citation statements)

References 17 publications

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“…LiDAR works by counting the time between acquiring backscattered energy from a pulsed laser beam and using these time measurements together with the speed of light in air to compute distances to objects and surfaces in the surrounding environment. 13 As the main form of 3D perception for many autonomous driving systems, LiDAR plays an important role in localization, 14 object classification, 15 scene segmentation, 16 and traversability estimation. 17 Compared to cameras, LiDAR is advantageous for navigational tasks because it can be used to directly measure the distance to obstacles which enables robots to navigate effectively while avoiding obstacles.…”

Section: Related Work 21 Lidar Technology In Roboticsmentioning

confidence: 99%

Analysis of LiDAR configurations on off-road semantic segmentation performance

Chen

Dabbiru

et al. 2023

Autonomous Systems: Sensors, Processing and Security for Ground, Air, Sea, and Space Vehicles and Infrastructure 2023

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LiDAR-based 3D semantic segmentation is one of the most widely used perception methods to support scene understanding of self-driving vehicles. Most publicly available LiDAR datasets for driving scene segmentation, such as SemanticKITTI, nuScenes, and SemanticPOSS, provide only a single type of LiDAR configuration. Therefore, testing a trained model with a different channel configuration than the training dataset is sometimes inevitable in real-world applications. Despite the significance of this LiDAR channel mismatch problem in the machine learning pipeline, little research has focused on investigating the impact of the LiDAR configuration shift on a model's test performance. This paper aims to provide referenceable baseline experiments for the LiDAR configuration shifts. We explore the effect of using different LiDAR channels when training and testing a 3D LiDAR point cloud semantic segmentation model, utilizing Cylinder3D for the experiments. A Cylinder3D model is trained and tested on simulated 3D LiDAR point cloud datasets created using the Mississippi State University Autonomous Vehicle Simulator (MAVS) and 32, 64 channel 3D LiDAR point clouds of the RELLIS-3D dataset collected in a real-world off-road environment. Our experimental results demonstrate that sensor and spatial domain shifts significantly impact the performance of LiDAR-based semantic segmentation models. In the absence of spatial domain changes between training and testing, models trained and tested on the same sensor type generally exhibited better performance. Moreover, higher-resolution sensors showed improved performance compared to those with lower-resolution ones. However, results varied when spatial domain changes were present. In some cases, the advantage of a sensor's higher resolution led to better performance both with and without sensor domain shifts. In other instances, the higher resolution resulted in overfitting within a specific domain, causing a lack of generalization capability and decreased performance when tested on data with different sensor configurations.

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Section: Related Work 21 Lidar Technology In Roboticsmentioning

confidence: 99%

Analysis of LiDAR configurations on off-road semantic segmentation performance

Chen

Dabbiru

et al. 2023

Autonomous Systems: Sensors, Processing and Security for Ground, Air, Sea, and Space Vehicles and Infrastructure 2023

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“…Real time mapping is most often embedded in SLAM. Chen et al [12] are using a vertical laser scanner to build the model with a SLAM process. [13] applied a mobile phone and a set of fixed actions to produce a 2D floor plan and a representative 3D model.…”

Section: A Indoor Modeling and Reconstructionmentioning

confidence: 99%

Furniture Free Mapping using 3D Lidars

He,

Hou,

Schwertfeger

2019

Preprint

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Mobile robots depend on maps for localization, planning, and other applications. In indoor scenarios, there is often lots of clutter present, such as chairs, tables, other furniture, or plants. While mapping this clutter is important for certain applications, for example navigation, maps that represent just the immobile parts of the environment, i.e. walls, are needed for other applications, like room segmentation or longterm localization. In literature, approaches can be found that use a complete point cloud to remove the furniture in the room and generate a furniture free map. In contrast, we propose a Simultaneous Localization And Mapping (SLAM)-based mobile laser scanning solution. The robot uses an orthogonal pair of Lidars. The horizontal scanner aims to estimate the robot position, whereas the vertical scanner generates the furniture free map. There are three steps in our method: point cloud rearrangement, wall plane detection and semantic labeling. In the experiment, we evaluate the efficiency of removing furniture in a typical indoor environment. We get 99.60% precision in keeping the wall in the 3D result, which shows that our algorithm can remove most of the furniture in the environment. Furthermore, we introduce the application of 2D furniture free mapping for room segmentation.

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“…As it has lower power consumption, smaller size, and lower cost compared to general 3D laser scanners, it is widely used in autonomous systems such as robot positioning [2], navigation [3], and mobile mapping [4,5] through the simultaneous localization and mapping (SLAM) technique [6]. Many systems, such as the light detection and ranging (LiDAR)-based SLAM system [7,8], employ several 2D LRFs simultaneously to perform sensor fusion, which reinforces the concern of extrinsic calibration for fusing all the LiDAR point cloud into a global reference frame. Moreover, the performances of mapping and SLAM are sensitive to calibration errors, especially when they have a long working range, so that small rotation errors can produce significant distortions in the map [9].…”

Section: Introductionmentioning

confidence: 99%

“…For example, in References [19,20], the three-DOF extrinsic calibration of multiple LRFs on the scanning plane was achieved by matching the target motion trajectories in the overlapping scanning region of the LRFs. However, for many applications, such as 3D mapping applications, there may be large angles between scanning planes [8,12], and then a six-DOF extrinsic calibration needs to be done. The authors of Reference [12] made a special facility based on the scanning planes of multiple LRFs to ensure all the LRFs were able to scan the small V-shaped targets mounted on the facility, and then they used the centers of the targets as control points for the extrinsic calibration.…”

Section: Introductionmentioning

confidence: 99%

Extrinsic Calibration of 2D Laser Rangefinders Using an Existing Cuboid-Shaped Corridor as the Reference

Yin

Liu

et al. 2018

Sensors

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Laser rangefinders (LRFs) are widely used in autonomous systems for indoor positioning and mobile mapping through the simultaneous localization and mapping (SLAM) approach. The extrinsic parameters of multiple LRFs need to be determined, and they are one of the key factors impacting system performance. This study presents an extrinsic calibration method of multiple LRFs that requires neither extra calibration sensors nor special artificial reference landmarks. Instead, it uses a naturally existing cuboid-shaped corridor as the calibration reference, and it hence needs no additional cost. The present method takes advantage of two types of geometric constraints for the calibration, which can be found in a common cuboid-shaped corridor. First, the corresponding point cloud is scanned by the set of LRFs. Second, the lines that are scanned on the corridor surfaces are extracted from the point cloud. Then, the lines within the same surface and the lines within two adjacent surfaces satisfy the coplanarity constraint and the orthogonality constraint, respectively. As such, the calibration problem is converted into a nonlinear optimization problem with the constraints. Simulation experiments and experiments based on real data verified the feasibility and stability of the proposed method.

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Real-time 3D Mobile Mapping for the Built Environment

Cited by 9 publications

References 17 publications

Analysis of LiDAR configurations on off-road semantic segmentation performance

Analysis of LiDAR configurations on off-road semantic segmentation performance

Furniture Free Mapping using 3D Lidars

Extrinsic Calibration of 2D Laser Rangefinders Using an Existing Cuboid-Shaped Corridor as the Reference

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