In this paper, we describe a method for global localization in a previously unvisited environment using only a schematic floor plan as a prior map. The floor plan need not be a precision map-it can be the sort of image found in buildings to guide people or aid evacuation. The core idea is to identify features that are stable across both a drawn floor plan and robot point-of-view LIDAR data, for example wall intersections, which appear as corners from overhead and as vertical lines from the ground. We introduce a factor graph-based global localization method that uses these features as landmarks. The detections of such descriptorless features are noisy and often ambiguous. We therefore propose robust data association based on a pairwise measurement consistency check and max-mixtures error model. We evaluate the resulting system in a real-world indoor environment, demonstrating performance comparable to a baseline system that uses a conventional LIDAR-based prior map.

For online robots, incremental SLAM algorithms offer huge potential computational savings over batch algorithms. The dominant incremental algorithms are iSAM and iSAM2 which offer radically different approaches to computing incremental updates, balancing issues like 1) the need to re-linearize, 2) changes in the desirable variable marginalization order, and 3) the underlying conceptual approach (i.e. the “matrix” story versus the “factor graph” story). In this paper, we propose a new incremental algorithm that computes solutions with lower absolute error and generally provides lower error solutions for a fixed computational budget than either iSAM or iSAM2. Key to AprilSAM's performance are a new dynamic variable reordering algorithm for fast incremental Cholesky factorizations, a method for reducing the work involved in backsubstitutions, and a new algorithm for deciding between incremental and batch updates.

Mobile robotic teams require robust communication in order to coordinate effectively, which is a challenge given the dynamic, unpredictable nature of mobile ad hoc networks (MANET). These networks are subject to rapidly varying link qualities as robots move through their environment. Improving the robustness of these point-to-point links leads to greater overall network performance, which in turn allows the robots to perform their mission more effectively. In this work, we present a forward error correction (FEC) technique that exploits latency tolerance in network traffic to provide consistent packet delivery performance even on low-quality links. Our proposed system estimates link quality based on recent packet reception history and uses that estimate to determine FEC encoding strength. Furthermore, this system provides a novel Quality of Service (QoS) mechanism that trades latency tolerance for more reliable, lower overhead transmission. We evaluate the effectiveness of this technique in a real-world robotic testbed.