CASIA OpenIR  > 模式识别国家重点实验室  > 机器人视觉
Robust 3D Indoor Map Building via RGB-D SLAM with Adaptive IMU Fusion on Robot
Meng XR(孟馨蕊)1,2; Gao W(高伟)1,2; Hu ZY(胡占义)1,2
2018
Conference NameInternational Conference on Image and Graphics
Pages454-465
Conference Date2017.9
Conference Place上海
AbstractBuilding a 3D map of indoor environment is a prerequisite for various applications, ranging from service robot to augmented reality, where RGB-D SLAM is a commonly used technique. To efficiently and robustly build a 3D map via RGB-D SLAM on robot, or the RGB-D sensor mounted on a moving robot, the following two key issues must be addressed: How to reliably estimate the robot’s pose to align partial models on the fly, and how to design the robot’s movement patterns in large environment to effectively reduce error accumulation and to increase building efficiency. To address these two issues in this work, we propose an algorithm to adaptively fuse the IMU information with the visual tracking for the first issue, and design two robot movement patterns for the second issue. The preliminary experiments on a TurtleBot2 robot platform show that our RGB-D SLAM system works well even for difficult situations such as weaktextured space, or presence of pedestrians. 
KeywordCamera Pose Estimation Rgb-d Slam Imu Robot Movement Pattern Calibration
Indexed ByEI
Language英语
Document Type会议论文
Identifierhttp://ir.ia.ac.cn/handle/173211/20948
Collection模式识别国家重点实验室_机器人视觉
Affiliation1.中国科学院自动化所
2.中国科学院大学
Recommended Citation
GB/T 7714
Meng XR,Gao W,Hu ZY. Robust 3D Indoor Map Building via RGB-D SLAM with Adaptive IMU Fusion on Robot[C],2018:454-465.
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