基於圖優化同時定位地構建與移動物體追蹤功能之多感測器融合行動機人

Graph-Based SLAM with Moving Object Tracking Mobile Robot using Multi-Sensory Fusion

吳謝浩(Xiehao Wu)

指導教授 ： 羅仁權

國立臺灣大學/電機資訊學院/電機工程學研究所/碩士(2014年)

https://doi.org/10.6342/NTU.2014.02623

摘要

本論文主旨在於發展在室內環境可以同時完成定位地圖構建 (SLAM) 與移動物體追蹤功能的行動機器人。SLAM可以幫助機器人定位與構建周圍環境、移動物體追蹤功能則把外界環境區分為靜止部份和移動部份。根據靜態地圖能夠偵測移動物體，同時在追蹤移動物體時又能夠幫助區分物體是否為靜態物。在機器人以及移動物體軌跡優化的框架下，本文把基於圖優化的SLAM延伸到了基於圖優化的SLAM與移動物體追蹤，從而同時對機器人軌跡以及移動物體軌跡進行優化。基於移動物體的量測，對移動物體未來行為以及過去行為進行推測，使得對於移動物體不同時刻的觀測可以互相幫助，從而得到更好的移動物體軌跡估測。對於機器人在複雜室內環境實現SLAM與移動物體追蹤會碰到的難題，例如移動物體的形狀和特點各異，很難利用先驗知識去建模，並且在複雜室內環境進行數據關聯也十分困難。本文提出的基於長數據序列的移動物體偵測在不需要對移動物體有先驗知識的情況下，完成移動物體偵測。在此方法下即使是十分細微的移動也會被偵測出來。另外，多感測器融合方法提高了數據關聯的精准度。實驗結果證明了本文的多感測器融合基於圖優化SLAM與移動物體追蹤方法可以在複雜室內環境中實現，把移動物體整合到基於圖優化SLAM中對比不整合移動物體的圖優化SLAM，降低了機器人姿態估測的不確定性。

關鍵字

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

並列摘要

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.

並列關鍵字

SLAM ； Moving Object Tracking ； Trajectory Optimization ； Multi-Sensory Fusion ； Cluttered Indoor Environment

參考文獻

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