Salience-guided Ground Factor for Robust Localization of Delivery Robots in Complex Urban Environments

In urban environments for delivery robots, particularly in areas such as campuses and towns, many custom features defy standard road semantic categorizations. Addressing this challenge, our paper introduces a method leveraging Salient Object Detection (SOD) to extract these unique features, employing them as pivotal factors for enhanced robot loop closure and localization. Traditional geometric feature-based localization is hampered by fluctuating illumination and appearance changes. Our preference for SOD over semantic segmentation sidesteps the intricacies of classifying a myriad of non-standardized urban features. To achieve consistent ground features, the Motion Compensate IPM (MC-IPM) technique is implemented, capitalizing on motion for distortion compensation and subsequently selecting the most pertinent salient ground features through moment computations. For thorough evaluation, we validated the saliency detection and localization performances to the real urban scenarios. Project page: https://sites.google.com/view/salient-ground-feature/home.

Mesh-based Photorealistic and Real-time 3D Mapping for Robust Visual Perception of Autonomous Underwater Vehicle

This paper proposes a photorealistic real-time dense 3D mapping system that utilizes a learning-based image enhancement method and mesh-based map representation. Due to the characteristics of the underwater environment, where problems such as hazing and low contrast occur, it is hard to apply conventional simultaneous localization and mapping (SLAM) methods. Furthermore, for sensitive tasks like inspecting cracks, photorealistic mapping is very important. However, the behavior of Autonomous Underwater Vehicle (AUV) is computationally constrained. In this paper, we utilize a neural network-based image enhancement method to improve pose estimation and mapping quality and apply a sliding window-based mesh expansion method to enable lightweight, fast, and photorealistic mapping. To validate our results, we utilize real-world and indoor synthetic datasets. We performed qualitative validation with the real-world dataset and quantitative validation by modeling images from the indoor synthetic dataset as underwater scenes.

ReFeree: Radar-based efficient global descriptor using a Feature and Free space for Place Recognition

Radar is highlighted for robust sensing capabilities in adverse weather conditions (e.g. dense fog, heavy rain, or snowfall). In addition, Radar can cover wide areas and penetrate small particles. Despite these advantages, Radar-based place recognition remains in the early stages compared to other sensors due to its unique characteristics such as low resolution, and significant noise. In this paper, we propose a Radarbased place recognition utilizing a descriptor called ReFeree using a feature and free space. Unlike traditional methods, we overwhelmingly summarize the Radar image. Despite being lightweight, it contains semi-metric information and is also outstanding from the perspective of place recognition performance. For concrete validation, we test a single session from the MulRan dataset and a multi-session from the Oxford Radar RobotCar and the Boreas dataset.

Robust Imaging Sonar-based Place Recognition and Localization in Underwater Environments

Place recognition using SOund Navigation and Ranging (SONAR) images is an important task for simultaneous localization and mapping(SLAM) in underwater environments. This paper proposes a robust and efficient imaging SONAR based place recognition, SONAR context, and loop closure method. Unlike previous methods, our approach encodes geometric information based on the characteristics of raw SONAR measurements without prior knowledge or training. We also design a hierarchical searching procedure for fast retrieval of candidate SONAR frames and apply adaptive shifting and padding to achieve robust matching on rotation and translation changes. In addition, we can derive the initial pose through adaptive shifting and apply it to the iterative closest point (ICP) based loop closure factor. We evaluate the performance of SONAR context in the various underwater sequences such as simulated open water, real water tank, and real underwater environments. The proposed approach shows the robustness and improvements of place recognition on various datasets and evaluation metrics. Supplementary materials are available at https://github.com/sparolab/sonar_context.git.

Edge-guided Multi-domain RGB-to-TIR image Translation for Training Vision Tasks with Challenging Labels

The insufficient number of annotated thermal infrared (TIR) image datasets not only hinders TIR image-based deep learning networks to have comparable performances to that of RGB but it also limits the supervised learning of TIR image-based tasks with challenging labels. As a remedy, we propose a modified multidomain RGB to TIR image translation model focused on edge preservation to employ annotated RGB images with challenging labels. Our proposed method not only preserves key details in the original image but also leverages the optimal TIR style code to portray accurate TIR characteristics in the translated image, when applied on both synthetic and real world RGB images. Using our translation model, we have enabled the supervised learning of deep TIR image-based optical flow estimation and object detection that ameliorated in deep TIR optical flow estimation by reduction in end point error by 56.5\% on average and the best object detection mAP of 23.9\% respectively. Our code and supplementary materials are available at https://github.com/rpmsnu/sRGB-TIR.

Low-cost Thermal Mapping for Concrete Heat Monitoring

Robotics has been widely applied in smart construction for generating the digital twin or for autonomous inspection of construction sites. For example, for thermal inspection during concrete curing, continual monitoring of the concrete temperature is required to ensure concrete strength and to avoid cracks. However, buildings are typically too large to be monitored by installing fixed thermal cameras, and post-processing is required to compute the accumulated heat of each measurement point. Thus, by using an autonomous monitoring system with the capability of long-term thermal mapping at a large construction site, both cost-effectiveness and a precise safety margin of the curing period estimation can be acquired. Therefore, this study proposes a low-cost thermal mapping system consisting of a 2D range scanner attached to a consumer-level inertial measurement unit and a thermal camera for automated heat monitoring in construction using mobile robots.

ViViD++: Vision for Visibility Dataset

In this paper, we present a dataset capturing diverse visual data formats that target varying luminance conditions. While RGB cameras provide nourishing and intuitive information, changes in lighting conditions potentially result in catastrophic failure for robotic applications based on vision sensors. Approaches overcoming illumination problems have included developing more robust algorithms or other types of visual sensors, such as thermal and event cameras. Despite the alternative sensors' potential, there still are few datasets with alternative vision sensors. Thus, we provided a dataset recorded from alternative vision sensors, by handheld or mounted on a car, repeatedly in the same space but in different conditions. We aim to acquire visible information from co-aligned alternative vision sensors. Our sensor system collects data more independently from visible light intensity by measuring the amount of infrared dissipation, depth by structured reflection, and instantaneous temporal changes in luminance. We provide these measurements along with inertial sensors and ground-truth for developing robust visual SLAM under poor illumination. The full dataset is available at: https://visibilitydataset.github.io/

DeepLO: Geometry-Aware Deep LiDAR Odometry

Recently, learning-based ego-motion estimation approaches have drawn strong interest from studies mostly focusing on visual perception. These groundbreaking works focus on unsupervised learning for odometry estimation but mostly for visual sensors. Compared to images, a learning-based approach using Light Detection and Ranging (LiDAR) has been reported in a few studies where, most often, a supervised learning framework is proposed. In this paper, we propose a novel approach to geometry-aware deep LiDAR odometry trainable via both supervised and unsupervised frameworks. We incorporate the Iterated Closest Point (ICP) algorithm into a deep-learning framework and show the reliability of the proposed pipeline. We provide two loss functions that allow switching between supervised and unsupervised learning depending on the ground-truth validity in the training phase. An evaluation using the KITTI and Oxford RobotCar dataset demonstrates the prominent performance and efficiency of the proposed method when achieving pose accuracy.

Generic Camera Attribute Control using Bayesian Optimization

Cameras are the most widely exploited sensor in both robotics and computer vision communities. Despite their popularity, two dominant attributes (i.e., gain and exposure time) have been determined empirically and images are captured in very passive manner. In this paper, we present an active and generic camera attribute control scheme using Bayesian optimization. We extend from our previous work [1] in two aspects. First, we propose a method that jointly controls camera gain and exposure time. Secondly, to speed up the Bayesian optimization process, we introduce image synthesis using the camera response function (CRF). These synthesized images allowed us to diminish the image acquisition time during the Bayesian optimization phase, substantially improving overall control performance. The proposed method is validated both in an indoor and an outdoor environment where light condition rapidly changes. Supplementary material is available at https://youtu.be/XTYR_Mih3OU .

Complex Urban LiDAR Data Set

This paper presents a Light Detection and Ranging (LiDAR) data set that targets complex urban environments. Urban environments with high-rise buildings and congested traffic pose a significant challenge for many robotics applications. The presented data set is unique in the sense it is able to capture the genuine features of an urban environment (e.g. metropolitan areas, large building complexes and underground parking lots). Data of two-dimensional (2D) and threedimensional (3D) LiDAR, which are typical types of LiDAR sensors, are provided in the data set. The two 16-ray 3D LiDARs are tilted on both sides for maximal coverage. One 2D LiDAR faces backward while the other faces forwards to collect data of roads and buildings, respectively. Raw sensor data from Fiber Optic Gyro (FOG), Inertial Measurement Unit (IMU), and the Global Positioning System (GPS) are presented in a file format for vehicle pose estimation. The pose information of the vehicle estimated at 100 Hz is also presented after applying the graph simultaneous localization and mapping (SLAM) algorithm. For the convenience of development, the file player and data viewer in Robot Operating System (ROS) environment were also released via the web page. The full data sets are available at: http://irap.kaist.ac.kr/dataset. In this website, 3D preview of each data set is provided using WebGL.