In this letter, we propose a point cloud structural similarity-based loop detection method for underwater Simultaneous Localization and Mapping using sonar sensors. Existing sonar-based loop detection approaches often rely on 2D projection and keypoint extraction, which can lead to data loss and poor performance in feature-scarce environments. Additionally, methods based on neural networks or Bag-of-Words require extensive preprocessing, such as model training or vocabulary creation, reducing adaptability to new environments. To address these challenges, our method directly utilizes 3D sonar point clouds without projection and computes point-wise structural feature maps based on geometry, normals, and curvature. By leveraging rotation-invariant similarity comparisons, the proposed approach eliminates the need for keypoint detection and ensures robust loop detection across diverse underwater terrains. We validate our method using two real-world datasets: the Antarctica dataset obtained from deep underwater and the Seaward dataset collected from rivers and lakes. Experimental results show that our method achieves the highest loop detection performance compared to existing keypoint-based and learning-based approaches while requiring no additional training or preprocessing.

We propose GOTPR, a robust place recognition method designed for outdoor environments where GPS signals are unavailable. Unlike existing approaches that use point cloud maps, which are large and difficult to store, GOTPR leverages scene graphs generated from text descriptions and maps for place recognition. This method improves scalability by replacing point clouds with compact data structures, allowing robots to efficiently store and utilize extensive map data. In addition, GOTPR eliminates the need for custom map creation by using publicly available OpenStreetMap data, which provides global spatial information. We evaluated its performance using the KITTI360Pose dataset with corresponding OpenStreetMap data, comparing it to existing point cloud-based place recognition methods. The results show that GOTPR achieves comparable accuracy while significantly reducing storage requirements. In city-scale tests, it completed processing within a few seconds, making it highly practical for real-world robotics applications. More information can be found at https://donghwijung.github.io/GOTPR_page/.

Sound can be utilized to gather information about vehicles approaching a Non-Line-of-Sight (NLoS) region that remains hidden from Line-of-Sight (LoS) sensors due to its reflective and diffractive characteristics, like a radar. However, due to the inability to determine the location of NLoS vehicles in previous studies, it has not been possible to construct a sound-based active emergency braking system. This paper introduces a novel approach for localization of vehicles approaching in NLoS regions through sound. Specifically, a new particle filter method incorporating Acoustic-Spatial Pseudo-Likelihood (ASPLE) has been proposed to track objects using both acoustic and spatial information from the ego vehicle. Also, the Acoustic Recognition based Invisible-target Localization (ARIL) dataset, which is the firstly providing the location of the NLoS vehicle as ground truth using Bird’s Eye View camera, is proposed. The proposed method is validated using two datasets: the ARIL dataset and the Occluded Vehicle Acoustic Detection Dataset (OVAD) dataset. The proposed method exhibited remarkable performance in localizing NLoS targets in both datasets, predicting the location of the vehicle in the NLoS region. Lastly, the analysis of how the reflection of sound affects to the proposed method, highlighting variations based on the spatial situations, and demonstrate the empirical convergence of the method is described. Our code and dataset is available at https://github.com/mingujeon/NLoSVehicleLocalization.

In this letter, we propose a point cloud structural similarity-based loop detection method for underwater Simultaneous Localization and Mapping using sonar sensors. Existing sonar-based loop detection approaches often rely on 2D projection and keypoint extraction, which can lead to data loss and poor performance in feature-scarce environments. Additionally, methods based on neural networks or Bag-of-Words require extensive preprocessing, such as model training or vocabulary creation, reducing adaptability to new environments. To address these challenges, our method directly utilizes 3D sonar point clouds without projection and computes point-wise structural feature maps based on geometry, normals, and curvature. By leveraging rotation-invariant similarity comparisons, the proposed approach eliminates the need for keypoint detection and ensures robust loop detection across diverse underwater terrains. We validate our method using two real-world datasets: the Antarctica dataset obtained from deep underwater and the Seaward dataset collected from rivers and lakes. Experimental results show that our method achieves the highest loop detection performance compared to existing keypoint-based and learning-based approaches while requiring no additional training or preprocessing.

We propose GOTPR, a robust place recognition method designed for outdoor environments where GPS signals are unavailable. Unlike existing approaches that use point cloud maps, which are large and difficult to store, GOTPR leverages scene graphs generated from text descriptions and maps for place recognition. This method improves scalability by replacing point clouds with compact data structures, allowing robots to efficiently store and utilize extensive map data. In addition, GOTPR eliminates the need for custom map creation by using publicly available OpenStreetMap data, which provides global spatial information. We evaluated its performance using the KITTI360Pose dataset with corresponding OpenStreetMap data, comparing it to existing point cloud-based place recognition methods. The results show that GOTPR achieves comparable accuracy while significantly reducing storage requirements. In city-scale tests, it completed processing within a few seconds, making it highly practical for real-world robotics applications. More information can be found at https://donghwijung.github.io/GOTPR_page/.

Sound can be utilized to gather information about vehicles approaching a Non-Line-of-Sight (NLoS) region that remains hidden from Line-of-Sight (LoS) sensors due to its reflective and diffractive characteristics, like a radar. However, due to the inability to determine the location of NLoS vehicles in previous studies, it has not been possible to construct a sound-based active emergency braking system. This paper introduces a novel approach for localization of vehicles approaching in NLoS regions through sound. Specifically, a new particle filter method incorporating Acoustic-Spatial Pseudo-Likelihood (ASPLE) has been proposed to track objects using both acoustic and spatial information from the ego vehicle. Also, the Acoustic Recognition based Invisible-target Localization (ARIL) dataset, which is the firstly providing the location of the NLoS vehicle as ground truth using Bird’s Eye View camera, is proposed. The proposed method is validated using two datasets: the ARIL dataset and the Occluded Vehicle Acoustic Detection Dataset (OVAD) dataset. The proposed method exhibited remarkable performance in localizing NLoS targets in both datasets, predicting the location of the vehicle in the NLoS region. Lastly, the analysis of how the reflection of sound affects to the proposed method, highlighting variations based on the spatial situations, and demonstrate the empirical convergence of the method is described. Our code and dataset is available at https://github.com/mingujeon/NLoSVehicleLocalization.

Deep reinforcement learning has been used to solve complex tasks in various fields, particularly in robotics control. However, agents trained using deep reinforcement learning have a problem of taking overconfident actions, even when the input state is far from the learned state distribution. This restricts deep reinforcement learning from being applied to real-world environments as overconfident actions in unlearned situations can result in catastrophic events; such as the collision of an autonomous vehicle. To address this, the agents should know “what they do not know” and choose an action by considering not only the state but also its uncertainty. In this study, we propose a novel uncertainty-conditioned policy (UNICON) inspired by the human behavior of changing policies according to uncertainty, e.g., slowing a car on a narrow road that has never been visited before. Our experimental results demonstrate that the proposed method is robust to unfamiliar scenarios that are not seen during training.

Autonomous vehicles are increasingly utilized in diverse industries, relying heavily on perception systems to interpret their surroundings for decision-making and control. While Line-of-Sight perception technologies have advanced significantly, Non-Line-of-Sight (NLoS) perception remains a critical challenge. Current systems struggle to detect objects in NLoS scenarios, such as pedestrians or vehicles suddenly appearing from behind obstacles, leading to accidents, particularly at narrow T-junctions in urban environments. To address this, mmWave radar has emerged as a promising sensor for NLoS perception due to its ability to capture reflections and estimate the location of dynamic objects in occluded areas. However, previous researches are limited to controlled settings or single objects, with challenges like multipath reflections requiring precise spatial analysis for real-world use. In this paper, we propose a localization method for multi-dynamic NLoS pedestrians using ray tracing on 2D radar point clouds obtained from mm Wave radar in outdoor environments. The approach involves inferring spatial information from static points, performing ray tracing for dynamic points, and applying noise filtering and clustering to estimate pedestrian locations. Validation on a custom-built test bed demonstrates the effectiveness of the method, establishing a foundation for advanced NLoS perception technologies in real-world driving.

We propose GOTPR, a robust place recognition method designed for outdoor environments where GPS signals are unavailable. Unlike existing approaches that use point cloud maps, which are large and difficult to store, GOTPR leverages scene graphs generated from text descriptions and maps for place recognition. This method improves scalability by replacing point clouds with compact data structures, allowing robots to efficiently store and utilize extensive map data. In addition, GOTPR eliminates the need for custom map creation by using publicly available OpenStreetMap data, which provides global spatial information. We evaluated its performance using the KITTI360Pose dataset with corresponding OpenStreetMap data, comparing it to existing point cloud-based place recognition methods. The results show that GOTPR achieves comparable accuracy while significantly reducing storage requirements. In city-scale tests, it completed processing within a few seconds, making it highly practical for real-world robotics applications. More information can be found at https://donghwijung.github.io/GOTPR_page/.

Pedestrians Localization in Non-Line-of-Sight (NLoS) regions within urban environments poses a significant challenge for autonomous driving systems. While mmWave radar has demonstrated potential for detecting objects in such scenarios, the 2D radar point cloud (PCD) data is susceptible to distortions caused by multipath reflections, making accurate spatial inference difficult. Additionally, although camera images provide high-resolution visual information, they lack depth perception and cannot directly observe objects in NLoS regions. In this paper, we propose a novel framework that interprets radar PCD through road layout inferred from camera for localization of NLoS pedestrians. The proposed method leverages visual information from the camera to interpret 2D radar PCD, enabling spatial scene reconstruction. The effectiveness of the proposed approach is validated through experiments conducted using a radar-camera system mounted on a real vehicle. The localization performance is evaluated using a dataset collected in outdoor NLoS driving environments, demonstrating the practical applicability of the method.