Real-time dense mapping for online processing and navigation

Autonomous robots require accurate localizations and dense mappings for motion planning. We consider the navigation scenario where the dense representation of the robot surrounding must be immediately available, and require that the system is capable of an instantaneous map correction if a loop closure is detected by the localization module. To satisfy the real-time processing requirement of online robotics applications, our presented system bounds the algorithmic complexity of the localization pipeline by restricting the number of variables to be optimized at each time instant. A dense map representation along with a local dense map reconstruction strategy is also proposed. Despite the limits that are imposed by the real-time requirement and planning safety, the mapping quality of our method is comparable to other competitive methods. For implementations, we additionally introduce a few engineering considerations, such as the system architecture, the variable initialization, the memory management, the image processing, and so forth, to improve the system performance. Extensive experimental validations of our presented system are performed on the KITTI and NewCollege datasets, and through an online experiment around the Hong Kong University of Science and Technology (HKUST) university campus. We release our implementation as open-source robot operating system (ROS) packages for the benefit of the community.