Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Most present localization algorithms are either range or vision-based. In many environments, only one type of sensor cannot often ensure successful localization; furthermore, using low-priced range sensors instead of expensive, but accurate, laser scanners often lead to poor performance.  proposes an MCL-based localization method that robustly estimates the robot pose with fusion of the range information from a low-cost IR scanner and the SIFT based visual information gathered using a mono camera. With sensor fusion, the rough pose estimation from range-based sensors is compensated by the vision-based sensors and slow object recognition can be overcome by the frequent update of the range information. In order to synchronize the two sensors with different bandwidths, the encoder information gathered during object recognition is exploited.  also suggests a method for evaluating localization performance that is based on the normalized probability of a vision sensor model. Various experiments show that the proposed algorithm can estimate the robot pose reasonably ll and can accurately evaluate the localization performance.
['Byung Doo Yim', 'Yong Ju Lee', 'Jae Bok Song', 'Woojin Chung']
I am writing a research paper on a idea Mobile Robot Localization Using Fusion of Object Recognition and Range Information
[{"from": "human", "value": "I am writing a research paper on a idea Mobile Robot Localization Using Fusion of Object Recognition and Range Information"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Most present localization algorithms are either range or vision-based. In many environments, only one type of sensor cannot often ensure successful localization; furthermore, using low-priced range sensors instead of expensive, but accurate, laser scanners often lead to poor performance.  proposes an MCL-based localization method that robustly estimates the robot pose with fusion of the range information from a low-cost IR scanner and the SIFT based visual information gathered using a mono camera. With sensor fusion, the rough pose estimation from range-based sensors is compensated by the vision-based sensors and slow object recognition can be overcome by the frequent update of the range information. In order to synchronize the two sensors with different bandwidths, the encoder information gathered during object recognition is exploited.  also suggests a method for evaluating localization performance that is based on the normalized probability of a vision sensor model. Various experiments show that the proposed algorithm can estimate the robot pose reasonably ll and can accurately evaluate the localization performance."}]