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«MULTI-CAMERA SIMULTANEOUS LOCALIZATION AND MAPPING Brian Sanderson Clipp A dissertation submitted to the faculty of the University of North Carolina ...»

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Low power VSLAM for embedded systems such as cell phones also remains a challenge. Klein and Murray’s cell phone PTAM (Klein and Murray, 2009) shows that VSLAM can be performed on these limited systems in real-time when the mapped area is very small.

However, wider area mapping on these extremely limited systems presents many difficulties. Feature extraction for loop detection requires significant resources, either a high per

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bundle adjustment is also a compute intensive operation. Map simplification techniques like the ones presented by Klein and Murray (Klein and Murray, 2009) will be needed to reduce the number of variables to optimize and reduce the necessary computation.

A scalable solution to the visual SLAM problem will open up many new possibilities.

Robots will be able to navigate unknown areas with low cost, non-emissive sensors, which do not interfere with each other. Vehicles will be able to drive themselves using sensors that are within the budget of the average consumer. Even now, some vehicle makers are developing stereo vision based systems that will stop the vehicle to prevent crashes and warn of pedestrians stepping into the vehicle’s path. The military will be able to deploy small robots to map buildings before soldiers enter them. This will allow the soldiers to get a better impression of the building and its inhabitants, hopefully reducing casualties, both civilian and military, through better situational awareness. This indoor mapping technology will be directly applicable in search and rescue. Robots using VSLAM will be able to explore areas too dangerous for human searchers, discovering survivors that could not otherwise be found. These are just a few of the many ways that VSLAM will change the way we interact with the world.

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