發(fā)布時(shí)間：2018-04-27 07:29

  本文選題：激光雷達(dá) + 同時(shí)定位與建圖�。� 參考：《浙江大學(xué)》2017年碩士論文

【摘要】：基于激光雷達(dá)的障礙物檢測(cè)與地圖構(gòu)建是無(wú)人駕駛系統(tǒng)中環(huán)境感知的重要組成部分。當(dāng)前主流采用的Velodyne HDL64E激光雷達(dá)具有體積大、價(jià)格高的缺點(diǎn),為改善這一問(wèn)題,本文提出使用多個(gè)小激光雷達(dá)組合的方式進(jìn)行地圖構(gòu)建與障礙物檢測(cè)。本文分析了 VelodyneHDL32E和VLP16激光雷達(dá)在不同安裝方式下的掃描精度,提出了多個(gè)激光雷達(dá)之間的組合安裝方式和標(biāo)定方法。為得到車體運(yùn)動(dòng)軌跡和地圖,本文實(shí)現(xiàn)了以激光雷達(dá)為傳感器的SLAM子系統(tǒng),即從點(diǎn)云中提取“線特征點(diǎn)”和“面特征點(diǎn)”用于幀間最近鄰匹配,通過(guò)最小化匹配誤差求出無(wú)人車幀間運(yùn)動(dòng)量,此外通過(guò)地圖配準(zhǔn)和閉環(huán)優(yōu)化兩個(gè)步驟減小累積誤差。除了車體軌跡和地圖,SLAM子系統(tǒng)輸出的去畸變點(diǎn)云可進(jìn)行多幀融合獲得更加精確、致密的點(diǎn)云,有助于正、負(fù)障礙物檢測(cè)。本文采用主流的柵格屬性地圖表示障礙物分布,通過(guò)分析正障礙物點(diǎn)云空間分布特征對(duì)正障礙物分類。此外,本文提出了負(fù)障礙物的三個(gè)局部結(jié)構(gòu)特征,通過(guò)檢測(cè)負(fù)障礙物候選線段,并對(duì)候選線段聚類處理獲得負(fù)障礙物區(qū)域。實(shí)際數(shù)據(jù)集上的定性實(shí)驗(yàn)表明基于激光雷達(dá)的SLAM系統(tǒng)能夠獲得準(zhǔn)確的車體軌跡和高精度點(diǎn)云地圖。此外在越野環(huán)境下的定量試驗(yàn)表明多激光雷達(dá)組合與多幀融合的方法能顯著提升點(diǎn)云密度,提高正障礙物和負(fù)障礙物檢測(cè)效果。
[Abstract]:Obstacle detection and map construction based on lidar is an important part of environment perception in unmanned systems. The current mainstream Velodyne HDL64E lidar has the disadvantages of large volume and high price. In order to improve this problem, this paper proposes to use multiple small lidar combinations to construct maps and detect obstacles. In this paper, the scanning accuracy of VelodyneHDL32E and VLP16 lidar under different installation modes is analyzed, and the combined installation mode and calibration method between several lidar radars are put forward. In order to obtain the motion track and map of the car body, a SLAM subsystem based on lidar sensor is implemented in this paper, namely, "line feature points" and "surface feature points" are extracted from the point cloud for the nearest neighbor matching between frames. By minimizing the matching error, the motion amount between the frames of the unmanned vehicle is obtained, and the cumulative error is reduced by map registration and closed-loop optimization. In addition to the car-body trajectory and map slam subsystem output dedistorted point clouds can be fused in multiple frames to obtain more accurate and dense point clouds, which is helpful for both positive and negative obstacle detection. In this paper, the main raster attribute map is used to represent the distribution of obstacles, and by analyzing the spatial distribution characteristics of point cloud of positive obstacles, the positive obstacles are classified. In addition, three local structural features of negative obstacles are presented in this paper. The candidate segments of negative obstacles are detected, and the negative obstacle regions are obtained by clustering the candidate segments. The qualitative experiments on the actual data set show that the SLAM system based on lidar can obtain accurate vehicle track and high accuracy point cloud map. In addition, quantitative experiments in off-road environment show that the combination of multi-lidar and multi-frame fusion can significantly improve the point cloud density and improve the effectiveness of both positive and negative obstacle detection.
【學(xué)位授予單位】：浙江大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：TN958.98
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本文編號(hào)：1809814