Using regions of interest to track landmarks for RGBD simultaneous localisation and mapping
The simultaneous localisation and mapping (SLAM) algorithm have been widely used for autonomous navigation of robots. A type of visual SLAM that is popular among the researchers is RGBD SLAM. However processing immense image data to identify and track landmarks in RGBD SLAM can be computationally expensive for smaller robots. This dissertation presents an alternate method to reduce the computational time. The proposed algorithm extracts features from a region of interest (ROI) to track landmarks for RGBD SLAM. This strategy is compared to the traditional method of extracting features from an entire image. The ROI algorithm is implemented via a pre-processing algorithm, which is then integrated into the RGBD SLAM framework. The pre-processing pipeline implements image processing algorithms in three stages to process the data. Stage one uses a ROI algorithm to detect ROIs in an image. For visual SLAM such as RGBD SLAM, objects that are highly detailed are used as landmarks. Hence the ROI algorithm is designed to detect ROIs containing highly detailed objects. Stage two extracts features from the image and stage three uses feature matching algorithms to re-identify a ROI. Once a ROI has been successfully re-identified, it is stored and categorised as a landmark for RGBD SLAM. Scale invariant feature transform (SIFT), speeded up robust features (SURF) and orientated FAST and rotated BRIEF (ORB) are three feature extraction algorithms that are used in stage two. The outcomes from this study revealed that the pipeline was able to successfully create a database of landmarks which can be re-identified in subsequent frames. In addition, the results showed that when the pipeline is configured such that SURF features are used with a bigger ROI, RGBD SLAM produced more accurate results in determining the position of the robot compared to the traditional method of extracting features from an entire image. However, this strategy requires more computational time. The findings further revealed that this strategy still out performs the traditional method when the number of features extracted is reduced. This indicated that this strategy performs more robustly compared to the traditional method in environments that can contain few features. The method presented in this study did not improve the computational time of RGBD SLAM but did improve the accuracy in localizing the robot.
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