Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:   describe a system for use on a mobile robot that detects potential loop closures using both visual and spatial appearance of local scenes. Loop closing is the act of correctly asserting that a vehicle has returned to a previously visited location. Current approaches rely heavily on vehicle pose estimates to prompt loop closure. Paradoxically, these approaches are least reliable when the need for accurate loop closure detection is the greatest. Our underlying approach relies instead upon matching distinctive ‘signatures’ of individual local scenes to prompt loop closure. A key advantage of this method is that it is entirely independent of the navigation and or mapping process and so is entirely unaffected by gross errors in pose estimation. Another advantage, which is explored in this paper, is the possibility to enhance robustness of loop closure detection by incorporating heterogeneous sensory observations.  show how a description of local spatial appearance (using laser rangefinder data) can be combined with visual descriptions to form multi-sensory signatures of local scenes which enhance loop-closure detection.
['Kin Leong Ho', 'Paul Newman']
I am writing a research paper on a idea Loop closure detection in SLAM by combining visual and spatial appearance
[{"from": "human", "value": "I am writing a research paper on a idea Loop closure detection in SLAM by combining visual and spatial appearance"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:   describe a system for use on a mobile robot that detects potential loop closures using both visual and spatial appearance of local scenes. Loop closing is the act of correctly asserting that a vehicle has returned to a previously visited location. Current approaches rely heavily on vehicle pose estimates to prompt loop closure. Paradoxically, these approaches are least reliable when the need for accurate loop closure detection is the greatest. Our underlying approach relies instead upon matching distinctive \u2018signatures\u2019 of individual local scenes to prompt loop closure. A key advantage of this method is that it is entirely independent of the navigation and or mapping process and so is entirely unaffected by gross errors in pose estimation. Another advantage, which is explored in this paper, is the possibility to enhance robustness of loop closure detection by incorporating heterogeneous sensory observations.  show how a description of local spatial appearance (using laser rangefinder data) can be combined with visual descriptions to form multi-sensory signatures of local scenes which enhance loop-closure detection."}]