低动态工作空间中多会话RGBD SLAM框架

需积分: 9 148 浏览量更新于2024-08-26 收藏 3.16MB PDF 举报

"这篇论文提出了一种在低动态工作空间中进行多会话RGBD SLAM（同时定位与建图）的框架，旨在解决自主移动机器人在环境发生变化时的映射问题。" 文章主要讨论了在动态环境中，由于工作空间不可避免的变化，对自主移动机器人进行地图构建的重要性。作者提出了一个适用于低动态环境的RGBD SLAM框架，该框架能够持续更新并保持地图的最新状态。这里的低动态环境指的是环境变化相对较慢的情况。关键知识点： 1. **RGBD SLAM**：RGBD SLAM是机器人定位和建图的一种方法，它利用来自RGBD相机的数据（包括彩色图像和深度信息）来同时估计机器人的运动轨迹和构建环境地图。 2. **多会话**：在多会话设置中，机器人多次访问同一区域，每次访问都会更新或扩展之前构建的地图。这有助于处理环境随时间变化的情况。 3. **姿态图模型**：环境的主要模型是一个多会话姿态图，它随着机器人多次访问而演变。姿态图中的节点代表机器人的位置，边则表示位置之间的相对关系。 4. **姿态图修剪**：当对应于特定姿态的3D点云扫描过期时，姿态图中的相应节点会被删除。这是为了确保地图反映的是最新的环境状态。 5. **新区域探索**：当机器人探索新区域时，其新的位置信息会被添加到姿态图中，从而不断扩展和更新地图。 6. **过期扫描识别模块**：通过分析不同时间点的扫描数据，这个模块能检测出环境的变化，识别出哪些扫描数据已经不再反映当前环境。 7. **环境变化检测**：环境变化的检测对于保持地图的准确性和实时性至关重要。通过过期扫描的识别，系统能有效地更新地图，适应环境的动态变化。此框架对于在室内或受控环境中的服务机器人、自动驾驶车辆等应用具有实际意义，因为这些场景虽然存在一定的动态变化，但通常变化速度较慢，适合采用这样的SLAM策略。通过这种框架，机器人可以更好地适应和理解其周围环境，提高其自主导航和任务执行能力。

information for better poses conﬁguration estimation. So in

these works, anchor n odes [9], weak links [1,6] are introduced

to solve the problem. The vision based SLAM in low dynamic

environment has also been studied. In Ref. [10], multiple poses

formed a view cluster, in which the images with the similar

view would be updated over time. This method can tell

whether a frame is out-of-dated but cannot show which part

has been changed as it was a sparse visual feature based

method.

Most existing methods dealing with SLAM in dynamic

environment is based on 2D laser SLAM. In Refs. [11,12], the

set of scans in global coordinates was updated by sampling

after each new session to build an in-dated map. In their work,

poses were estimated by SLAM at the ﬁrst session. For the

later sessions, the poses were estimated by localization, not

included in the SLAM framework. In Refs. [8,13], both works

described the dynamic with each cell in grid occupancy map

having an inde pendent Markov Model. In Ref. [7], a dynamic

environment map was modeled as a pose graph. After each

session, the out-of-dated poses are identiﬁed and removed

based on 2D occupancy grid map built from the laser data. In

Ref. [14], the poses related to the low dynamics were removed

to enhance the robust of the optimizer.

In the context of RGBD SLAM, most works apply the

graph mode l, followed by a global optimization backe nd. In

Ref. [15], both visual features and depth information are

employed to form an edge in the pose graph. Besides the

formulation, an environment measurement model was pro-

posed for pose graph edge selection in Ref. [3]. In Ref. [4],a

dense visual odometry is used as frontend to formulate the

pose graph, which is more accurate than sparse featur e based

visual odometry. In Ref. [5], non-rigid deformation is com-

bined with the pose graph optimization for globally consistent

dense map, which takes the map mesh into consideration.

Extension of these RGBD SLAM systems to multi-session can

be achieved by applying the methods developed in Refs.

[1,6,9]. But the detection of dynamics by simply using the

laser based method is difﬁcult, as the methods employed an

occupancy grid map for information fusion and de-noise.

When it comes to the case of RGBD sensor, the 3D o ccu-

pancy grid map is intractable due to the high complexity. So

methods should be developed on the raw senso r data, making

the problem more challenging.

Besides the mechanism for dealing with dynamic envi-

ronment, a framework for RGBD SLAM also needs node

pruning to keep the computational complexity noncumulative.

Fig. 1. A comparison of the reconstructed low dynamic environment in point cloud with 10 sessions mapping using multi-session SLAM without considering the

low dynamics (top) and proposed framework considering the low dynamics (bottom). One can see that the book, box, bottles and plastic bags are repeated, making

the scene with incorrect duplicated information. The book and the chip can are highlighted using light and dark orange rectangles. Their out-of-dated positions are

highlighted using red rectangles. The arrows demonstrate the correspondence.

92 Y. Wang et al. / CAAI Transactions on Intelligence Technology 1 (2016) 90e103

剩余13页未读，继续阅读

donghanruchen

粉丝: 0
资源: 37

低动态工作空间中多会话RGBD SLAM框架

ORB-SLAM2-RGBD-DENSE-MAP-data.tar

SLAM实践：ORB_SLAM2与D435---rgbd_tum.cc

ubuntu16.04运行RGBD slam

安装rgbd slam

ubuntu三维激光slam安装

如何安装ros-melodic-rgbd-launch

orb-slam3运行rgbd+imu

vins-fusion rgbd 的相关介绍

ORB-SLAM3 运行 D435i 的 ORB-SLAM3指令

基于RGBD的slam框架有哪些

最新资源