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Supplementary Material 1

Additional file 1. Study questionnaire. Description of data: The questionnaire used for this study was developed specifically for this study. This questionnaire examined patients’ participation in sports activities pre- and post-operatively, patients’ current satisfaction with daily life and sports activities, and related patient-reported outcomes.

This study addresses the problem of visual change detection using a 3D point cloud (PC) map acquired by a car-like robot. With recent advances in long-term autonomous navigation, change detection under global viewpoint uncertainty has become a topic of considerable interest. In our study, we extend the traditional two-level pipeline of change detection: (1) scene registration and (2) scene comparison, to enable scalable and efficient change detection. In the traditional pipeline, the registration stage is required to align a given scene pair (i.e., query and reference PC maps) that are taken at different times into the same coordinate system, before comparing the two PCs. However, the registration stage is a time-consuming step, which makes it harder to realize a scalable change detection. Our key concept is to transform every query or reference PC beforehand into an invariant coordinate system, which should be predefined and invariant to environment changes (e.g., dynamic objects, clutters, the mapper vehicle's trajectories), so as to enable a direct comparison of spatial layout between the two different maps. The proposed framework employs an efficient bag-of-local-features (BoLF) scene model and realizes a scalable joint viewpoint-change detection. Change detection experiments using a publicly available cross-season NCLT dataset validate the efficacy of the approach.

This paper addresses the problem of bag-of-words (BoW)-based vehicle self-localization from a novel 3D point cloud data perspective. In this novel problem domain, a major limitation of BoW scene models is lack of spatial information: BoW methods ignore the spatial layout information for the features, and hence, they have severely limited descriptive ability. We address this issue by explicitly modeling the input point cloud from a unique viewpoint; the pose of the local feature is defined with respect to this unique viewpoint, and can be viewed as an additional invariant feature for discriminative scene retrieval. Experimental results from a challenging cross-season vehicle localization scenario verify the efficacy of the proposed approach.

Map retrieval, the problem of similarity search over a large collection of 3D pointset maps previously built by mobile robots, is crucial for autonomous navigation in indoor and outdoor environments. Bag-of-words (BoW) methods constitute a popular approach to map retrieval; however, these methods have extremely limited descriptive ability because they ignore the spatial layout information of the local features. The main contribution of this paper is an extension of the bag-of-words map retrieval method to enable the use of spatial information from local features. Our strategy is to explicitly model a unique viewpoint of an input local map; the pose of the local feature is defined with respect to this unique viewpoint, and can be viewed as an additional invariant feature for discriminative map retrieval. Specifically, we wish to determine a unique viewpoint that is invariant to moving objects, clutter, occlusions, and actual viewpoints. Hence, we perform scene parsing to analyze the scene structure, and consider the "center" of the scene structure to be the unique viewpoint. Our scene parsing is based on a Manhattan world assumption that imposes a quasi-Manhattan world constraint to enable the robust detection of a scene structure that is invariant to clutter and moving objects. Experimental results using the publicly available University of Michigan North Campus Long-Term Vision and LIDAR Dataset (NCLT dataset [1]) validate the efficacy of the proposed approach.

本論文は，性能設計に向けた護岸築造工事に伴う沖積粘性土の挙動を把握するために実施した動態観測，土質試験結果について考察を加えたものである。海上での護岸築造工事の動態観測は，地盤変形が大きいことによる計器の破損や荒天時のデータ欠如等の問題が生じ，計測方法・測定値の評価方法等に多くの工夫が必要であった。当現場では，各種埋設計器の設計・施工に工夫を施すことにより，海上現場で精度の高い沖積粘性土の挙動に関する動態観測データが得られた。この護岸築造における動態観測結果を詳細に考察した結果，事前に実施した土の段階載荷による圧密試験で得られたe-logp曲線に沿って圧密が進んでいることが分かった。また，土のせん断ひずみは，護岸盛土の進行に伴い深度，地層により，その大きさ，速度は異なり，荷重載荷後直ちに変形は終了しないことが分かった。