Graph-Based SLAM with Moving Object Tracking Mobile Robot using Multi-Sensory Fusion
Date Issued
2014
Date
2014
Author(s)
Wu, Xiehao
Abstract
The objective of this thesis is to develop simultaneous localization and mapping (SLAM) with capability of tracking moving object in indoor environments. SLAM can help build environment map, while detection and tracking of moving object separate the environment into static and dynamic parts. The map can help detect the moving object, on the other hand, the moving object tracking can help separate the stationary and moving objects, thus we can separate them in the map. By augmenting the moving objects state and related constraints into the robot and objects graph, the general graph-based framework for SLAM issues can be extended to jointly optimize the SLAM and moving object tracking result. By incorporating the moving object prediction and moving object Retro-BestGuess, the later measurement of moving object can help the estimation of the previous state and vice versa. Consequently, the trajectory of robot together with the trajectories of moving objects is optimized. Furthermore, the SLAM with moving object tracking issues in the cluttered indoor environment are analyzed, the moving object may have different size and characteristics difficult to modelling, and the data association is difficult. The multi-frame moving object detection is applied to detect the moving object without the need of prior knowledge, by which even the slightly movement can be detected. The multi-sensor fusion methodologies can help increase the data association accuracy. The experimental results shown that our algorithm is feasible in cluttered indoor environment, graph-based SLAM incorporating moving objects can decrease the pose estimation uncertainty compare to the one not incorporating them.
Subjects
同時定位地圖構建
移動物體追蹤
軌跡優化
多感測器融合
複雜室內環境
Type
thesis
File(s)![Thumbnail Image]()
Loading...
Name
ntu-103-R01921082-1.pdf
Size
23.32 KB
Format
Adobe PDF
Checksum
(MD5):d2f492828514b1e18b40b9db2460c804