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.
Work within HAM belongs primarily to one of two groups, depending on whether the human and robot collaborate in-person during data collection (C-HAM), or whether the human provides input remotely or after the data collection (R-HAM). Many C-HAM techniques exist to address semantic @cite_14 @cite_11 and topological @cite_23 mapping. A number of approaches have also been proposed for integrating semantic and topological information, along with human trackers @cite_3 , interaction models @cite_12 , and appearance information @cite_19 , into @cite_22 , which are organized in a hierarchical manner.
@cite_14 @cite_22 @cite_3 @cite_19 @cite_23 @cite_12 @cite_11
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In this paper, a method for tracking and following people in indoor environments by using a multi-sensor mobile platform is presented. The general use of the approach is in Human-Augmented Mapping (HAM). Laser range data are employed for people detection by searching for legs. A person following algorithm is then implemented allowing the robot to keep track of and pursue a human user. Results from experiments in a real indoor environment are provided to validate the approach. It is shown that the method can be successfully integrated into interactively controlled mapping and navigation systems. Semantic information can help robots understand unknown environments better. In order to obtain semantic information efficiently and link it to a metric map, we present a new robot semantic mapping approach through human activity recognition in a human-robot coexisting environment. An intelligent mobile robot platform called ASCCbot creates a metric map while wearable motion sensors attached to the human body are used to recognize human activities. Combining pre-learned models of activity-furniture correlation and location-furniture correlation, the robot determines the probability distribution of the furniture types through a Bayesian framework and labels them on the metric map. Computer simulations and real experiments demonstrate that the proposed approach is able to create a semantic map of an indoor environment effectively. A framework for robot semantic mapping through human activity recognition.Human activity recognition is realized through wearable motion sensors.Validated through both simulation and experiments. We describe a semantic mapping algorithm that learns human-centric environment models by interpreting natural language utterances. Underlying the approach is a coupled metric, topological, and semantic representation of the environment that enables the method to fuse information from natural language descriptions with low-level metric and appearance data. We extend earlier work with a novel formulation that incorporates spatial layout into a topological representation of the environment. We also describe a factor graph formulation of the semantic properties that encodes human-centric concepts such as type and colloquial name for each mapped region. The algorithm infers these properties by combining the user’s natural language descriptions with imageand laser-based scene classification. We also propose a mechanism to more effectively ground natural language descriptions of distant regions using semantic cues from other modalities. We describe how the algorithm employs this learned semantic information to propose valid topological hypotheses, leading to more accurate topological and metric maps. We demonstrate that integrating language with other sensor data increases the accuracy of the achieved spatial-semantic representation of the environment. Semantic information can help both humans and robots to understand their environments better. In order to obtain semantic information efficiently and link it to a metric map, we present a semantic mapping approach through human activity recognition in an indoor human-robot coexisting environment. An intelligent mobile robot platform can create a 2D metric map, while human activity can be recognized using motion data from wearable motion sensors mounted on a human subject. Combined with pre-learned models of activity-to-furniture type association and robot pose estimates, the robot can determine the distribution of the furniture types on the 2D metric map. Simulations and real world experiments demonstrate that the proposed method is able to create a reliable metric map with accurate semantic information.