Dataset Information

Have I Seen This Place Before? A Fast and Robust Loop Detection and Correction Method for 3D Lidar SLAM.

ABSTRACT: In this paper, we present a complete loop detection and correction system developed for data originating from lidar scanners. Regarding detection, we propose a combination of a global point cloud matcher with a novel registration algorithm to determine loop candidates in a highly effective way. The registration method can deal with point clouds that are largely deviating in orientation while improving the efficiency over existing techniques. In addition, we accelerated the computation of the global point cloud matcher by a factor of 2?4, exploiting the GPU to its maximum. Experiments demonstrated that our combined approach more reliably detects loops in lidar data compared to other point cloud matchers as it leads to better precision?recall trade-offs: for nearly 100% recall, we gain up to 7% in precision. Finally, we present a novel loop correction algorithm that leads to an improvement by a factor of 2 on the average and median pose error, while at the same time only requires a handful of seconds to complete.

SUBMITTER: Vlaminck M

PROVIDER: S-EPMC6339070 | biostudies-literature | 2018 Dec

REPOSITORIES: biostudies-literature

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Publications

Have I Seen This Place Before? A Fast and Robust Loop Detection and Correction Method for 3D Lidar SLAM.

Vlaminck Michiel M Luong Hiep H Philips Wilfried W

Sensors (Basel, Switzerland) 20181221 1

In this paper, we present a complete loop detection and correction system developed for data originating from lidar scanners. Regarding detection, we propose a combination of a global point cloud matcher with a novel registration algorithm to determine loop candidates in a highly effective way. The registration method can deal with point clouds that are largely deviating in orientation while improving the efficiency over existing techniques. In addition, we accelerated the computation of the glo ...[more]

PMID: 30577652

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Project description:With rapid urbanization, highly accurate and semantically rich virtualization of building assets in 3D become more critical for supporting various applications, including urban planning, emergency response and location-based services. Many research efforts have been conducted to automatically reconstruct building models at city-scale from remotely sensed data. However, developing a fully-automated photogrammetric computer vision system enabling the massive generation of highly accurate building models still remains a challenging task. One the most challenging task for 3D building model reconstruction is to regularize the noises introduced in the boundary of building object retrieved from a raw data with lack of knowledge on its true shape. This paper proposes a data-driven modeling approach to reconstruct 3D rooftop models at city-scale from airborne laser scanning (ALS) data. The focus of the proposed method is to implicitly derive the shape regularity of 3D building rooftops from given noisy information of building boundary in a progressive manner. This study covers a full chain of 3D building modeling from low level processing to realistic 3D building rooftop modeling. In the element clustering step, building-labeled point clouds are clustered into homogeneous groups by applying height similarity and plane similarity. Based on segmented clusters, linear modeling cues including outer boundaries, intersection lines, and step lines are extracted. Topology elements among the modeling cues are recovered by the Binary Space Partitioning (BSP) technique. The regularity of the building rooftop model is achieved by an implicit regularization process in the framework of Minimum Description Length (MDL) combined with Hypothesize and Test (HAT). The parameters governing the MDL optimization are automatically estimated based on Min-Max optimization and Entropy-based weighting method. The performance of the proposed method is tested over the International Society for Photogrammetry and Remote Sensing (ISPRS) benchmark datasets. The results show that the proposed method can robustly produce accurate regularized 3D building rooftop models.

Dataset Information

Have I Seen This Place Before? A Fast and Robust Loop Detection and Correction Method for 3D Lidar SLAM.

Publications

Have I Seen This Place Before? A Fast and Robust Loop Detection and Correction Method for 3D Lidar SLAM.

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