A Light Visual Mapping and Navigation Framework for Low-Cost Robots

We address the problems of localization, mapping, and guidance for robots with limited computational resources by combining vision with the metrical information given by the robot odometry. We propose in this article a novel light and robust topometric simultaneous localization and mapping framework using appearance-based visual loop-closure detection enhanced with the odometry. The main advantage of this combination is that the odometry makes the loop-closure detection more accurate and reactive, while the loop-closure detection enables the long-term use of odometry for guidance by correcting the drift. The guidance approach is based on qualitative localization using vision and odometry, and is robust to visual sensor occlusions or changes in the scene. The resulting framework is incremental, real-time, and based on cheap sensors provided on many robots (a camera and odometry encoders). This approach is, moreover, particularly well suited for low-power robots as it is not dependent on the image processing frequency and latency, and thus it can be applied using remote processing. The algorithm has been validated on a Pioneer P3DX mobile robot in indoor environments, and its robustness is demonstrated experimentally for a large range of odometry noise levels.