| Abstract: | 市地現況測量成果與圖解數化之地籍圖經過套疊分析，解決圖地不符之問題。現行地籍相關之現況測量多以經緯儀、衛星定位儀等儀器施測，但此方法耗費大量人力、時間成本，且其成果通常較局部，無法測得全面之現況資料，成果也較難以視覺化方式整體展示測量成果，尤其是用於市地狹窄、蜿蜒巷弄之現況測量亦遭遇許多限制。而光達LiDAR掃描能夠在短時間獲取高精度之點雲資料，具有高精度、高效率、可視覺化等優點，藉此辨識地物特徵並進而於內業執行現況測量。尤其是手持式光達掃描儀器，為MMS(Mobile Mapping System)移動式測繪的一種形式，其體積小、方便攜帶、掃描速度快、最重要的是操作者可以邊移動邊進行掃描，並採用SLAM演算法進行定位，不需要GNSS資料，故可深入室內、狹窄、蜿蜒環境中，補足地面光達不易探測之區域，藉此補助地面光達測量對於封閉環境或是空間狹隘的地區的限制。本研究欲提升手持式光達點雲精度輔助市地地籍現況測量，期望透過現有資料，進行手持式光達掃描點雲精度分析，並建立兩種手持式光達掃描儀器之率定方式，探討不同率定方式的差異及可行性，並藉由率定結果或後處理等方式提升手持式光達掃描之精度並應用於地籍測量之市地現況測量。
The registration results of the digitized graphic cadastral maps with urban land detail points are analyzed to solve the problem of inconsistent cadastral maps, land current situation and land register. The current detail points for cadastral survey are mostly surveyed by theodolites and satellite positioning instruments, but it is time-consuming and labor-intensive. The surveying results are usually local data and also difficult to visualize in 3-dimension model. Especially for the surveying of narrow and winding streets in urban area, there are also limited. LiDAR scanner can acquire high-precision point cloud data in a short time. It has the advantages of high accuracy, high efficiency, and visualization, so as to identify features of buildings to perform urban detail surveying. The handheld LiDAR scanner is a form of MMS (Mobile Mapping System), it is of small size, portability, fast scanning speed, and, most importantly, scanning while moving. It uses SLAM algorithm for positioning, without GNSS data, so it can be used in indoor, narrow, and winding environments to supplement areas where the terrestrial LiDAR is difficult to work, thereby assisting the terrestrial LiDAR scanning in closed environments or restricted spaces. This study is going to promote the accuracy of handheld LiDAR point clouds to assist in urban detail surveying for cadastral survey. It is expected to analyze the accuracy of handheld LiDAR point clouds through existing terrestrial LiDAR data, and to establish two calibration methods for handheld LiDAR scanning data, then to explore the differences and feasibility of two different calibration methods. Additionally, it will be hoped to promote the accuracy to assist in urban details surveying for cadastral survey by using the calibration results or post-processing methods to establish the error correction mode of handheld LiDAR scanning data. |
