Abstract

This paper proposes movement detection method between point clouds created by SFM software, without setting any onsite georeferenced points. SfM software, like Smart3DCaputure, PhotoScan, and Pix4D, are convenient for non-professional operator of photogrammetry, because these systems require simply specification of sequence of photos and output point clouds with colour index which corresponds to the colour of original image pixel where the point is projected. SfM software can execute aerial triangulation and create dense point clouds fully automatically. This is useful when monitoring motion of unstable slopes, or loos rocks in slopes along roads or railroads. Most of existing method, however, uses mesh-based DSM for comparing point clouds before/after movement and it cannot be applied in such cases that part of slopes forms overhangs. And in some cases movement is smaller than precision of ground control points and registering two point clouds with GCP is not appropriate. Change detection method in this paper adopts CCICP (Classification and Combined ICP) algorithm for registering point clouds before / after movement. The CCICP algorithm is a type of ICP (Iterative Closest Points) which minimizes point-to-plane, and point-to-point distances, simultaneously, and also reject incorrect correspondences based on point classification by PCA (Principle Component Analysis). Precision test shows that CCICP method can register two point clouds up to the 1 pixel size order in original images. Ground control points set in site are useful for initial setting of two point clouds. If there are no GCPs in site of slopes, initial setting is achieved by measuring feature points as ground control points in the point clouds before movement, and creating point clouds after movement with these ground control points. When the motion is rigid transformation, in case that a loose Rock is moving in slope, motion including rotation can be analysed by executing CCICP for a loose rock and background slope independently.

Highlights

  • Monitoring of loose rocks on slopes along the roads is quite important to keep traffic safety in Japan, where many roads are laid across mountainous area

  • Each point have colour index originated from colour of original image pixel

  • We have formerly proposed the evaluation method of correctness of shape of the point cloud created by SfM (Oda et al, 2015)

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Summary

INTRODUCTION

Monitoring of loose rocks on slopes along the roads is quite important to keep traffic safety in Japan, where many roads are laid across mountainous area. We have formerly proposed the evaluation method of correctness of shape of the point cloud created by SfM (Oda et al, 2015) The evaluation of this correctness uses the difference of point clouds with different source of images. Some tests with test sample captured under indoor situation results that correctness of point clouds can be almost same as pixel resolution of original images (Oda et al, 2015) This kind of registration can be applied to small movement detection of the slope, where some loose rock may fall from the slope. If point-cloud generation uses ground control points (GCPs), movement smaller than precision of GCPs cannot be measured Another problem is that most of existing method uses meshbased DSM for comparing point clouds before/after movement and it cannot be applied in such cases that part of slopes forms overhangs. This process can compensate the difference of coordinates between both point clouds

Categorization
Creating the monitoring point cloud
Point-Cloud Registration
Capturing reference images and monitoring images
Creating reference point clouds
Movement analysis
EXAMPLE OF MOVEMENT DETECTION
CONCLUSION AND FUTURE WORKS
Full Text
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