Static and Dynamic Objects Analysis as a 3D Vector Field

Jiang, Cansen; Paudel, Danda Pani; Fougerolle, Yohan; Fofi, David; Demonceaux, Cédric

doi:10.1109/3dv.2017.00035

Cited by 6 publications

(3 citation statements)

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“…Simultaneous localization and mapping with moving object tracking method is mainly used in robot scanning (Wang, Thorpe, 2002). A 3D flow field is computed to analyze the motions during ICP (Jiang et al, 2017b), and dynamic objects are detected by flow clustering.…”

Section: Moving Objects Analysismentioning

confidence: 99%

Moving Objects Aware Sensor Mesh Fusion for Indoor Reconstruction From a Couple of 2d Lidar Scans

Vallet

Demonceaux³

et al. 2020

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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Abstract. Indoor mapping attracts more attention with the development of 2D and 3D camera and Lidar sensor. Lidar systems can provide a very high resolution and accurate point cloud. When aiming to reconstruct the static part of the scene, moving objects should be detected and removed which can prove challenging. This paper proposes a generic method to merge meshes produced from Lidar data that allows to tackle the issues of moving objects removal and static scene reconstruction at once. The method is adapted to a platform collecting point cloud from two Lidar sensors with different scan direction, which will result in different quality. Firstly, a mesh is efficiently produced from each sensor by exploiting its natural topology. Secondly, a visibility analysis is performed to handle occlusions (due to varying viewpoints) and remove moving objects. Then, a boolean optimization allows to select which triangles should be removed from each mesh. Finally, a stitching method is used to connect the selected mesh pieces. Our method is demonstrated on a Navvis M3 (2D laser ranger system) dataset and compared with Poisson and Delaunay based reconstruction methods.

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Section: Moving Objects Analysismentioning

confidence: 99%

Moving Objects Aware Sensor Mesh Fusion for Indoor Reconstruction From a Couple of 2d Lidar Scans

Vallet

Demonceaux³

et al. 2020

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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“…Nevertheless, the presented system does not run in real time, requiring roughly 12 minutes to process a 70-frame (i.e., seven-second) sequence. A follow-up work from the same authors [9] improves the system's robustness to challenges such as partial occlusions by using 3D flow field analysis.…”

Section: Related Workmentioning

confidence: 99%

Robust Dense Mapping for Large-Scale Dynamic Environments

Bârsan

Liu

Pollefeys

et al. 2018

2018 IEEE International Conference on Robotics and Automation (ICRA)

130

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We present a stereo-based dense mapping algorithm for large-scale dynamic urban environments. In contrast to other existing methods, we simultaneously reconstruct the static background, the moving objects, and the potentially moving but currently stationary objects separately, which is desirable for high-level mobile robotic tasks such as path planning in crowded environments. We use both instance-aware semantic segmentation and sparse scene flow to classify objects as either background, moving, or potentially moving, thereby ensuring that the system is able to model objects with the potential to transition from static to dynamic, such as parked cars. Given camera poses estimated from visual odometry, both the background and the (potentially) moving objects are reconstructed separately by fusing the depth maps computed from the stereo input. In addition to visual odometry, sparse scene flow is also used to estimate the 3D motions of the detected moving objects, in order to reconstruct them accurately. A map pruning technique is further developed to improve reconstruction accuracy and reduce memory consumption, leading to increased scalability. We evaluate our system thoroughly on the well-known KITTI dataset. Our system is capable of running on a PC at approximately 2.5Hz, with the primary bottleneck being the instance-aware semantic segmentation, which is a limitation we hope to address in future work. The source code is available from the project website a .

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“…This article is an extended version of our previous work [Jiang et al, 2017c] and our contribution can be summarized as follows:…”

mentioning

confidence: 99%