How to divide floor plans or navigation maps into a semantic representation, such as rooms and corridors, is an important research question in fields such as human-robot interaction, place categorization, or semantic mapping. While most algorithms focus on segmenting robot built maps, those are not the only types of map a robot, or its user, can use. We have developed a method for segmenting maps from different modalities, focusing on robot built maps and hand-drawn sketch maps, and show better results than state of the art for both types.Our method segments the map by doing a convolution between the distance image of the map and a circular kernel, and grouping pixels of the same value. Segmentation is done by detecting ripple like patterns where pixel values vary quickly, and merging neighboring regions with similar values.We identify a flaw in segmentation evaluation metric used in recent works and propose a more consistent metric. We compare our results to ground-truth segmentations of maps from a publicly available dataset, on which we obtain a better Matthews correlation coefficient (MCC) than state of the art with 0.98 compared to 0.85 for a recent Voronoi-based segmentation method and 0.78 for the DuDe segmentation method. We also provide a dataset of sketches of an indoor environment, with two possible sets of ground truth segmentations, on which our method obtains a MCC of 0.82 against 0.40 for the Voronoibased segmentation method and 0.45 for DuDe.
In search and rescue missions, time is an important factor; fast navigation and quickly acquiring situation awareness might be matters of life and death. Hence, the use of robots in such scenarios has been restricted by the time needed to explore and build a map. One way to speed up exploration and mapping is to reason about unknown parts of the environment using prior information. While previous research on using external priors for robot mapping mainly focused on accurate maps or aerial images, such data are not always possible to get, especially indoor. We focus on emergency maps as priors for robot mapping since they are easy to get and already extensively used by firemen in rescue missions. However, those maps can be outdated, information might be missing, and the scales of rooms are typically not consistent.We have developed a formulation of graph-based SLAM that incorporates information from an emergency map. The graph-SLAM is optimized using a combination of robust kernels, fusing the emergency map and the robot map into one map, even when faced with scale inaccuracies and inexact start poses.We typically have more than 50% of wrong correspondences in the settings studied in this paper, and the method we propose correctly handles them. Experiments in an office environment show that we can handle up to 70% of wrong correspondences and still get the expected result. The robot can navigate and explore while taking into account places it has not yet seen. We demonstrate this in a test scenario and also show that the emergency map is enhanced by adding information not represented such as closed doors or new walls.
Lidars and cameras are the sensors most commonly used for Simultaneous Localization And Mapping (SLAM). However, they are not effective in certain scenarios, e.g. when fire and smoke are present in the environment. While radars are much less affected by such conditions, radar and lidar have rarely been compared in terms of the achievable SLAM accuracy. We present a principled comparison of the accuracy of a novel radar sensor against that of a Velodyne lidar, for localization and mapping. We evaluate the performance of both sensors by calculating the displacement in position and orientation relative to a ground-truth reference positioning system, over three experiments in an indoor lab environment. We use two different SLAM algorithms and found that the mean displacement in position when using the radar sensor was less than 0.037 m, compared to 0.011 m for the lidar. We show that while producing slightly less accurate maps than a lidar, the radar can accurately perform SLAM and build a map of the environment, even including details such as corners and small walls.
Simultaneous Localization And Mapping (SLAM) usually assumes the robot starts without knowledge of the environment. While prior information, such as emergency maps or layout maps, is often available, integration is not trivial since such maps are often out of date and have uncertainty in local scale. Integration of prior map information is further complicated by sensor noise, drift in the measurements, and incorrect scan registrations in the sensor map. We present the Auto-Complete Graph (ACG), a graph-based SLAM method merging elements of sensor and prior maps into one consistent representation. After optimizing the ACG, the sensor map’s errors are corrected thanks to the prior map, while the sensor map corrects the local scale inaccuracies in the prior map. We provide three datasets with associated prior maps: two recorded in campus environments, and one from a fireman training facility. Our method handled up to 40% of noise in odometry, was robust to varying levels of details between the prior and the sensor map, and could correct local scale errors of the prior. In field tests with ACG, users indicated points of interest directly on the prior before exploration. We did not record failures in reaching them.
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