Isolation wards operate in quarantine rooms to prevent cross-contamination caused by infectious diseases. Behind the benefits, medical personnel can have the infection risk from patients and the heavy workload due to the isolation. This work proposes a robot-assisted system to alleviate these problems in isolation wards. We conducted a survey about the medical staff's difficulties and envisioning robots. Using the investigation result, we devised three valuable services using two kinds of heterogeneous robots: telemedicine, emergency alert, and delivery services by care robots and delivery robots. Our system also provides user-interactive components such as a dashboard for medical staff and a patient app for inpatients. To manage the services efficiently, we suggest the robotic system based on a central control server and a hierarchical management architecture. Through a user study, we reviewed the usability of the developed system and its future directions.

Super-resolution of LiDAR range images is crucial to improving many downstream tasks such as object detection, recognition, and tracking. While deep learning has made a remarkable advances in super-resolution techniques, typical convolutional architectures limit upscaling factors to specific output resolutions in training. Recent work has shown that a continuous representation of an image and learning its implicit function enable almost limitless upscaling. However, the detailed approach, predicting values (depths) for neighbor pixels in the input and then linearly interpolating them, does not best fit the LiDAR range images since it does not fill the unmeasured details but creates a new image with regression in a high-dimensional space. In addition, the linear interpolation blurs sharp edges providing important boundary information of objects in 3-D points. To handle these problems, we propose a novel network, Implicit LiDAR Network (ILN), which learns not the values per pixels but weights in the interpolation so that the super-resolution can be done by blending the input pixel depths but with non-linear weights. Also, the weights can be considered as attentions from the query to the neighbor pixels, and thus an attention module in the recent Transformer architecture can be leveraged. Our experiments with a novel large-scale synthetic dataset demonstrate that the proposed network reconstructs more accurately than the state-of-the-art methods, achieving much faster convergence in training.

In this article, we present a novel localization method for multiple sources in indoor environments. Our approach can estimate different propagation paths, including the reflection and diffraction paths of sound waves based on a backward ray tracing technique. To estimate diffraction propagation paths, we combine a ray tracing algorithm with a uniform theory of diffraction model by exploiting the diffraction properties as propagation paths bend around the wedges of obstacles. We reconstruct the 3-D environments and wedges of obstacles in the precomputation phase and utilize these outcomes to generate primary, reflection, and diffraction acoustic rays in the runtime phase. We localize multiple sources when identifying the convergence regions of these acoustic rays based on Monte Carlo localization (MCL). Our approach supports not only stationary but also moving sources of human speech and clapping sounds. Our approach can also handle nonline-of-sight (NLOS) sources and distinguish between active and inactive source states. We evaluated and analyzed our algorithm in multiple scenarios containing obstacles and NLOS sources. Our approach can localize moving sources with the average of distance errors of 0.65 and 0.74 m in single and multiple source cases, respectively, in rooms, 7 m by 7 m in size with a height of 3 m; errors are measured according to the L2 distance between the estimated and actual source positions. We observed a 130% improvement of the localization accuracy over the prior work (J.-M. Valinet al.).

Vision aided dynamic exploration on bipedal robots poses an integrated challenge for perception and control. Rapid walking motions as well as the vibrations caused by the landing-foot contact-force introduce critical uncertainty in the visual-inertial system, which can cause the robot to misplace its feet placing on complex terrains and even fall over. In this paper, we present a streamlined integration of an efficient geometric footstep planner and the corresponding walking controller for a humanoid robot to dynamically walk across rough terrain at speeds up to 0.3 m/s. To handle perception uncertainty that arises during dynamic locomotion, we present a geometric safety scoring method in our footstep planner to optimally select feasible path candidates. In addition, the real-time performance of the perception pipeline allows for reactive locomotion such as generating a new corresponding swing leg trajectory in mid-gait if a sudden change in the terrain is detected. The proposed perception-control pipeline is evaluated and demonstrated with real experiments using a full-scale humanoid to traverse across various terrains.

We present a novel, robust sound source localization algorithm considering back-propagation signals. Sound propagation paths are estimated by generating direct and reflection acoustic rays based on ray tracing in a backward manner. We then compute the back-propagation signals by designing and using the impulse response of the backward sound propagation based on the acoustic ray paths. For identifying the 3D source position, we use a well-established Monte Carlo localization method. Candidates for a source position are determined by identifying convergence regions of acoustic ray paths. Those candidates are validated by measuring similarities between back-propagation signals, under the assumption that the back-propagation signals of different acoustic ray paths should be similar near the ground-truth sound source position. Thanks to considering similarities of back-propagation signals, our approach can localize a source position with an averaged error of 0.55 m in a room of 7 m by 7 m area with 3 m height in tested environments. We also place additional 67 dB and 77 dB white noise at the background, to test the robustness of our approach. Overall, we observe a 7 % to 100 % improvement in accuracy over the state-of-the-art method.

In this paper, we present a new approach, AKIMap, that uses an adaptive kernel inference for dense and sharp occupancy grid representations. Our approach is based on the multivariate kernel estimation, and we propose a simple, two-stage based method that selects an adaptive bandwidth matrix for an efficient and accurate occupancy estimation. To utilize correlations of occupancy observations given sparse and non-uniform distributions of point samples, we propose to use the covariance matrix as an initial bandwidth matrix, and then optimize the bandwidth matrix by adjusting its scale in an efficient, data-driven way for on-the-fly mapping. We demonstrate that the proposed technique estimates occupancy states more accurately than state-of-the-art methods given equal-data or equal-time settings, thanks to our adaptive inference. Furthermore, we show the practical benefits of the proposed work in on-the-fly mapping and observe that our adaptive approach shows the dense as well as sharp occupancy representations in a real environment.

In this paper, we present two novel approaches, super rays and culling region, for efficiently updating grid-based occupancy maps with point clouds. Rays, which traverse from the sensor origin to the sensor data, update the occupancy probabilities of a map representing an environment. Based on the ray model, we define a super ray as a representative ray to multiple rays having the same traversal patterns during the map updates. Our super rays utilize the geometric information of rays and reduce the number of points used for updating the map. For constructing super rays efficiently, we propose mapping lines for handling two- and three-dimensional cases from an observation that edges or grid points branch out the traversal patterns on the map. Furthermore, we introduce a culling region using the occupancy states of the updated map for reducing redundant computations occurred in updates. The super rays perform the update process in a single traversal, and the culling region reduces the number of unnecessary traversals for updating the map. As a result, our combined method improves the update performance without compromising any representation accuracy of a grid-based map. We test the update performance of the proposed method using public indoor and outdoor datasets. Our combined approach shows up to 11.8 times and 2.8 times performance improvement over the state-of-the-art update methods of grid-based maps in the indoor and outdoor scenes, respectively. Also, we compare the update speed and the representation accuracy of our method using the KITTI dataset over the state-of-the-art learning-based occupancy maps. In a navigation scenario that raw point clouds are acquired in 10 Hz, our method shows the best performance on the update speed and thus the highest representation accuracy within a given time.

A recent trend in optimal motion planning has broadened the research area toward the hybridization of sampling, optimization and grid-based approaches. We can expect that synergy from such integrations leads to overall performance improvement, but seamless integration and generalization is still an open problem. In this paper, we suggest a hybrid motion planning algorithm utilizing a sampling-based and optimization-based planner while simultaneously approximating a configuration free space. Unlike conventional optimization-based approaches, the proposed algorithm does not depend on a priori information or resolution-complete factors, e.g., a distance field. Ours instead learns spatial information on the fly by exploiting empirical information during the execution, and decentralizes the information over the constructed graph for efficient access. With the help of the learned information, our optimization-based local planner exploits the local area to identify the connectivity of configuration free space without depending on the precomputed domain knowledge. To show the novelty of proposed algorithm, we evaluate it against other asymptotic optimal planners in both synthetic and complex benchmarks with varying degrees of freedom. We also discuss the performance improvement, properties and limitations we have observed.

We present a novel approach, Super Ray, for efficiently updating map representations such as grids and octrees with point clouds. In this paper, we define a super ray for points as a representative ray to them with an associated frustum. A super ray is constructed in a way that updating those points has the same set of cells accessed during the map update process. As a result, we can perform the update process with a super ray in a single traversal on the map, resulting in performance improvement without compromising any representation accuracy of the map. For constructing super rays efficiently, we propose mapping lines for handling 2-D and 3-D cases from an observation that edges or grid points branch out the access pattern of updating the map. Our method is general enough to be applied for variety of occupancy map structures based on axis-aligned space subdivisions such as grids and octrees. We test our method into indoor and outdoor benchmarks, and achieve 2.5 times on average (up to 3.5 times) performance improvement over the state-of-the-art update method for OctoMap and grid maps.