The Peopleremover—Removing Dynamic Objects From 3-D Point Cloud Data by Traversing a Voxel Occupancy Grid

Schauer, Johannes; Nüchter, Andreas

doi:10.1109/lra.2018.2801797

Cited by 104 publications

(49 citation statements)

References 15 publications

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“…These methods mostly run offline and rely on a prebuilt map. Some methods use time-consuming voxel ray casting and require accurately aligned poses to clean the dense terrestrial laser scans [13], [25]. To alleviate the computational burden, visibility-based methods have been proposed [22], [31].…”

Section: Related Workmentioning

confidence: 99%

Moving Object Segmentation in 3D LiDAR Data: A Learning-based Approach Exploiting Sequential Data

Chen,

Li,

Mersch

et al. 2021

Preprint

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The ability to detect and segment moving objects in a scene is essential for building consistent maps, making future state predictions, avoiding collisions, and planning. In this paper, we address the problem of moving object segmentation from 3D LiDAR scans. We propose a novel approach that pushes the current state of the art in LiDAR-only moving object segmentation forward to provide relevant information for autonomous robots and other vehicles. Instead of segmenting the point cloud semantically, i.e., predicting the semantic classes such as vehicles, pedestrians, buildings, roads, etc., our approach accurately segments the scene into moving and static objects, i.e., distinguishing between moving cars vs. parked cars. Our proposed approach exploits sequential range images from a rotating 3D LiDAR sensor as an intermediate representation combined with a convolutional neural network and runs faster than the frame rate of the sensor. We compare our approach to several other state-of-the-art methods showing superior segmentation quality in urban environments. Additionally, we created a new benchmark for LiDAR-based moving object segmentation based on SemanticKITTI. We publish it to allow other researchers to compare their approaches transparently and we will publish our code.

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Section: Related Workmentioning

confidence: 99%

Moving Object Segmentation in 3D LiDAR Data: A Learning-based Approach Exploiting Sequential Data

Chen,

Li,

Mersch

et al. 2021

Preprint

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“…2) 3D Change Detection: Given well-aligned maps, a set difference operation can be conducted via extracting map-tomap complements [3,6]. Otherwise, visibility-based scan-tomap discrepancy comparison [1,7,8,16] has been a popular choice, because of the small covisible volume and inherent localization errors. Removert [8] leveraged range images of multiple window sizes.…”

Section: Related Workmentioning

confidence: 99%

“…2) Change detection under SLAM error: Change detection between two maps would be trivial if maps were perfectly aligned. Early works [5,3,6,7] in map change detection relied on the strong assumption of globally well-aligned maps with no error and avoided handling this ambiguity issue. Unfortunately, trajectory error inevitably occur in reality.…”

Section: Introductionmentioning

confidence: 99%

LT-mapper: A Modular Framework for LiDAR-based Lifelong Mapping

Kim¹,

Kim²

2021

Preprint

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Long-term 3D map management is a fundamental capability required by a robot to reliably navigate in the non-stationary real-world. This paper develops open-source, modular, and readily available LiDAR-based lifelong mapping for urban sites. This is achieved by dividing the problem into successive subproblems: multi-session SLAM (MSS), high/low dynamic change detection, and positive/negative change management. The proposed method leverages MSS and handles potential trajectory error; thus, good initial alignment is not required for change detection. Our change management scheme preserves efficacy in both memory and computation costs, providing automatic object segregation from a large-scale point cloud map. We verify the framework's reliability and applicability even under permanent year-level variation, through extensive real-world experiments with multiple temporal gaps (from day to year).

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“…In order to simplify the data volume, many algorithms have been provided so far. Among these algorithms, point cloud simplification by voxelization is the most widely used method, especially in reverse engineering [2][3][4]. For example, the grid-based simplification For the purpose of preserving the geometric features, this paper provides the FPPS algorithm, and FPPS algorithm processing starts with the extraction of key points from the point cloud.…”

Section: Introductionmentioning

confidence: 99%

Feature-Preserved Point Cloud Simplification Based on Natural Quadric Shape Models

et al. 2019

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With the development of 3D scanning technology, a huge volume of point cloud data has been collected at a lower cost. The huge data set is the main burden during the data processing of point clouds, so point cloud simplification is critical. The main aim of point cloud simplification is to reduce data volume while preserving the data features. Therefore, this paper provides a new method for point cloud simplification, named FPPS (feature-preserved point cloud simplification). In FPPS, point cloud simplification entropy is defined, which quantifies features hidden in point clouds. According to simplification entropy, the key points including the majority of the geometric features are selected. Then, based on the natural quadric shape, we introduce a point cloud matching model (PCMM), by which the simplification rules are set. Additionally, the similarity between PCMM and the neighbors of the key points is measured by the shape operator. This represents the criteria for the adaptive simplification parameters in FPPS. Finally, the experiment verifies the feasibility of FPPS and compares FPPS with other four-point cloud simplification algorithms. The results show that FPPS is superior to other simplification algorithms. In addition, FPPS can partially recognize noise.

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The Peopleremover—Removing Dynamic Objects From 3-D Point Cloud Data by Traversing a Voxel Occupancy Grid

Cited by 104 publications

References 15 publications

Moving Object Segmentation in 3D LiDAR Data: A Learning-based Approach Exploiting Sequential Data

Moving Object Segmentation in 3D LiDAR Data: A Learning-based Approach Exploiting Sequential Data

LT-mapper: A Modular Framework for LiDAR-based Lifelong Mapping

Feature-Preserved Point Cloud Simplification Based on Natural Quadric Shape Models

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