Abstract
Localization and Mapping are the mandatory functionalities of an autonomous mobile robot. These functionalities are implemented mostly using expensive laser range sensors which are characterized by long range, wide opening angle and precision. Recent developments in low-cost RGBD cameras make them suitable for robot mapping and localization. However, limitations like viewing angle, range and accuracy prevent them from replacing laser rangers in large areas. With such RGBD cameras, mapping large areas is hard but localization in large areas can be achieved by augmenting them with additional information. This makes the low-cost RGBD cameras suitable for a cost-effective localization solution, especially for the fleets of robots operating in large areas, where one robot equipped with a laser ranger is used in mapping while the other robots equipped with low-cost RGBD cameras for localization. Adaptive Monte Carlo Localization(AMCL) is the most widely used algorithm for mobile robot localization. Our proposed solution extends the AMCL to use a virtual laser generated from RGBD camera and visual markers to solve the issues concerning the global localization, reducing the uncertainty in pose estimates during tracking and kidnapped robot problem. Our approach is tested in large areas and the uncertainty during the localization is compared against the result from using a laser ranger.
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