Marker-free registration of forest terrestrial laser scanner data pairs with embedded confidence metrics

Journal Article · Mon Apr 04 00:00:00 EDT 2016 · IEEE Transactions on Geoscience and Remote Sensing

DOI:https://doi.org/10.1109/TGRS.2016.2539219· OSTI ID:1263883

Van Aardt, Jan ^[1]; Romanczyk, Paul ^[2]; van Leeuwen, Martin ^[3]; Kelbe, David ^[4]; Cawse-Nicholson, Kerry ^[5]

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1. Rochester Institute of Technology, Rochester, NY (United States)
2. Rochester Institute of Technology, Rochester, NY (United States); Aerospace Corp., El Segundo, CA (United States)
3. Rochester Institute of Technology, Rochester, NY (United States); Univ. College London, London (United Kingdom)
4. Rochester Institute of Technology, Rochester, NY (United States); Oak Ridge National Lab. (ORNL), Knoxville, TN (United States)
5. Rochester Institute of Technology, Rochester, NY (United States); Terracor, Johannesburg (South Africa)

Terrestrial laser scanning (TLS) has emerged as an effective tool for rapid comprehensive measurement of object structure. Registration of TLS data is an important prerequisite to overcome the limitations of occlusion. However, due to the high dissimilarity of point cloud data collected from disparate viewpoints in the forest environment, adequate marker-free registration approaches have not been developed. The majority of studies instead rely on the utilization of artificial tie points (e.g., reflective tooling balls) placed within a scene to aid in coordinate transformation. We present a technique for generating view-invariant feature descriptors that are intrinsic to the point cloud data and, thus, enable blind marker-free registration in forest environments. To overcome the limitation of initial pose estimation, we employ a voting method to blindly determine the optimal pairwise transformation parameters, without an a priori estimate of the initial sensor pose. To provide embedded error metrics, we developed a set theory framework in which a circular transformation is traversed between disjoint tie point subsets. This provides an upper estimate of the Root Mean Square Error (RMSE) confidence associated with each pairwise transformation. Output RMSE errors are commensurate with the RMSE of input tie points locations. Thus, while the mean output RMSE=16.3cm, improved results could be achieved with a more precise laser scanning system. This study 1) quantifies the RMSE of the proposed marker-free registration approach, 2) assesses the validity of embedded confidence metrics using receiver operator characteristic (ROC) curves, and 3) informs optimal sample spacing considerations for TLS data collection in New England forests. Furthermore, while the implications for rapid, accurate, and precise forest inventory are obvious, the conceptual framework outlined here could potentially be extended to built environments.

• Research Organization: Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
• Sponsoring Organization: USDOE Laboratory Directed Research and Development (LDRD) Program
• Grant/Contract Number: AC05-00OR22725
• OSTI ID: 1263883
• Journal Information: IEEE Transactions on Geoscience and Remote Sensing, Vol. 54, Issue 7; ISSN 0196-2892
• Publisher: IEEE
• Country of Publication: United States
• Language: English

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