Building large-scale, globally consistent maps is a challenging problem, made more difficult in environments with limited access, sparse features, or when using data collected by novice users. For such scenarios, where state-of-the-art mapping algorithms produce globally inconsistent maps, we introduce a systematic approach to incorporating sparse human corrections, which we term Human-in-the-Loop Simultaneous Localization and Mapping (HitL-SLAM). Given an initial factor graph for pose graph SLAM, HitL-SLAM accepts approximate, potentially erroneous, and rank-deficient human input, infers the intended correction via expectation maximization (EM), back-propagates the extracted corrections over the pose graph, and finally jointly optimizes the factor graph including the human inputs as human correction factor terms, to yield globally consistent large-scale maps. We thus contribute an EM formulation for inferring potentially rank-deficient human corrections to mapping, and human correction factor extensions to the factor graphs for pose graph SLAM that result in a principled approach to joint optimization of the pose graph while simultaneously accounting for multiple forms of human correction. We present empirical results showing the effectiveness of HitL-SLAM at generating globally accurate and consistent maps even when given poor initial estimates of the map.
Hand-Drawn sketches are natural means by which abstract descriptions of environments can be provided. They represent weak prior information about the scene, thereby enabling a robot to perform autonomous navigation and exploration when a full metrical description of the environment is not available beforehand. In this paper, we present an extensive evaluation of our navigation system that uses a sketch interface to allow the operator of a robot to draw a rough map of an indoor environment as well as a desired trajectory for the robot to follow. We employ a theoretical framework for sketch interpretation, in which associations between the sketch and the real world are modeled as local deformations of a suitable metric manifold. We investigate the effectiveness of our system and present empirical results from a set of experiments in real-world scenarios, focusing both on the navigation capabilities and the usability of the interface. In this paper, we propose an algorithm that enables robots to improve their spatial-semantic representation of the environment by engaging users in dialog. The algorithm aims to reduce the entropy in maps formulated based upon user-provided natural language descriptions (e.g., "The kitchen is down the hallway"). The robot's available information-gathering actions take the form of targeted questions intended to reduce the entropy over the grounding of the user's descriptions. These questions include those that query the robot's local surround (e.g., "Are we in the kitchen?") as well as areas distant from the robot (e.g., "Is the lab near the kitchen?"). Our algorithm treats dialog as an optimization problem that seeks to balance the information-theoretic value of candidate questions with a measure of cost associated with dialog. In this manner, the method determines the best questions to ask based upon expected entropy reduction while accounting for the burden on the user. We evaluate the entropy reduction based upon a joint distribution over a hybrid metric, topological, and semantic representation of the environment learned from user-provided descriptions and the robot's sensor data. We demonstrate that, by asking deliberate questions of the user, the method results in significant improvements in the accuracy of the resulting map. Robot localization is a one of the most important problems in robotics. Most of the existing approaches assume that the map of the environment is available beforehand and focus on accurate metrical localization. In this paper, we address the localization problem when the map of the environment is not present beforehand, and the robot relies on a hand-drawn map from a non-expert user. We addressed this problem by expressing the robot pose in the pixel coordinate and simultaneously estimate a local deformation of the hand-drawn map. Experiments show that we are able to localize the robot in the correct room with a robustness up to 80