Change Detection in Urban Streets by a Real Time Lidar Scanner and MLS Reference Data

Gálai, Bence; Benedek, Csaba

doi:10.1007/978-3-319-59876-5_24

Cited by 5 publications

(8 citation statements)

References 10 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Several Lidar devices, such as the Rotating multi-beam (RMB) sensors manufactured by Velodyne and Ouster, can provide high frame-rate point cloud streams containing accurate, but relatively sparse 3D geometric information from the environment. These point clouds can be used for infrastructure monitoring, urban planning [17], and SLAM [5].…”

Section: Proposed Methodsmentioning

confidence: 99%

Section: A Reference Methodsmentioning

confidence: 99%

“…Our second reference method follows a voxel occupancybased approach [17], where the detection accuracy and the ability to compensate minor registration errors depend on the chosen voxel resolution. As a core step of the algorithm, [17] applies a registration method between the point cloud pairs. For noise filtering and registration error elimination, a Markov Random Field (MRF) model is adopted which is defined in the range image domain [17].…”

Section: A Reference Methodsmentioning

confidence: 99%

“…As a core step of the algorithm, [17] applies a registration method between the point cloud pairs. For noise filtering and registration error elimination, a Markov Random Field (MRF) model is adopted which is defined in the range image domain [17].…”

Section: A Reference Methodsmentioning

confidence: 99%

See 3 more Smart Citations

ChangeGAN: A Deep Network for Change Detection in Coarsely Registered Point Clouds

Nagy

Kovács

Benedek

2021

IEEE Robot. Autom. Lett.

Self Cite

View full text Add to dashboard Cite

In this paper we introduce a novel change detection approach called ChangeGAN for coarsely registered point clouds in complex street-level urban environment.Our generative adversarial network-like (GAN) architecture compounds Siamese-style feature extraction, U-net-like use of multiscale features, and Spatial Trans-formation Network (STN) blocks for optimal transformation estimation. The input point clouds are represented by range images, which enables the use of 2D convolutional neural networks. The result is a pair of binary masks showing the change regions on each input range image, which can be backprojected to the input point clouds without loss of information.We have evaluated the proposed method on various challenging scenarios and we have shown its superiority against state-of-theart change detection methods.

show abstract

Section: Proposed Methodsmentioning

confidence: 99%

Section: A Reference Methodsmentioning

confidence: 99%

Section: A Reference Methodsmentioning

confidence: 99%

Section: A Reference Methodsmentioning

confidence: 99%

See 2 more Smart Citations

ChangeGAN: A Deep Network for Change Detection in Coarsely Registered Point Clouds

Nagy

Kovács

Benedek

2021

IEEE Robot. Autom. Lett.

Self Cite

View full text Add to dashboard Cite

show abstract

“…Map provides the background point cloud. Following our earlier approach [2], we transform the point clouds P RMB and P * Map into range images by ray tracing, and apply a Markov Random Field based binary change segmentation in the 2D image domain (Fig. 4).…”

Section: Search For Missing Objects Via Change Detectionmentioning

confidence: 99%

Exploitation of Dense MLS City Maps for 3D Object Detection

Zováthi

Nagy

Benedek

2020

Lecture Notes in Computer Science

Self Cite

View full text Add to dashboard Cite

In this paper we propose a novel method for the exploitation of High Density Localization (HDL) maps obtained by Mobile Laser Scanning in order to increase the performance of state-of-the-art real time dynamic object detection (RTDOD) methods utilizing Rotating Multi-Beam (RMB) Lidar measurements. First, we align the onboard measurements to the 3D HDL map with a multimodal point cloud registration algorithm operating in the Hough space. Next we apply a grid based probabilistic step to filter out the object regions on the RMB Lidar data which were falsely predicted as dynamic objects by RTDOD, although they are part of the static background scene. On the other hand, to find objects erroneously missed by the RTDOD predictions, we implement a Markov Random Field based point level change detection approach between the map and the current onboard measurement frame. Finally, to analyse the changed but previously unclassified segments of the RMB Lidar clouds, we apply a geometric blob separation and a Support Vector Machine based classification to distinguish the different object types. Comparative tests are provided in high traffic road sections of Budapest, Hungary, and we show an improvement of 5, 96% in precision, 9, 21% in recall and 7, 93% in F-score metrics against the state-of-the-art RTDOD algorithm.

show abstract

Multi-sensorial Environment Perception in Urban Environment

Benedek

2022

Communications in Computer and Information Science

View full text Add to dashboard Cite

Change Detection in Urban Streets by a Real Time Lidar Scanner and MLS Reference Data

Cited by 5 publications

References 10 publications

ChangeGAN: A Deep Network for Change Detection in Coarsely Registered Point Clouds

ChangeGAN: A Deep Network for Change Detection in Coarsely Registered Point Clouds

Exploitation of Dense MLS City Maps for 3D Object Detection

Multi-sensorial Environment Perception in Urban Environment

Contact Info

Product

Resources

About