61 accepted papers
Reliable 3D instance segmentation is fundamental to language-grounded robotic manipulation. Its critical application lies in cluttered environments, where occlusions, limited viewpoints, and noisy masks degrade perception. To address these challenges, we present Clutt3R-Seg, a zero-shot pipeline for…
Radar odometry is crucial for robust localization in challenging environments; however, the sparsity of reliable returns and distinctive noise characteristics impede its performance. This paper introduces geometrically-constrained radar-inertial odometry and mapping that jointly consolidates point a
In cluttered scenes with inevitable occlusions and incomplete observations, selecting informative viewpoints is essential for building a reliable representation. In this context, 3D Gaussian Splatting (3DGS) offers a distinct advantage, as it can explicitly guide the selection of subsequent viewpoin…
Accurate traversability assessment is critical for mobile robot motion planning, yet sensor occlusions and model limitations often compromise cost map reliability. Therefore, analyzing spatial uncertainty is essential for robust risk management. We propose a novel Maximum Entropy Deep Inverse Reinfo…
Despite the growing adoption of radar in robotics, the majority of research has been confined to homogeneous sensors, overlooking the integration and cross-modality challenges inherent in heterogeneous radar. This leads to significant difficulties in generalizing across diverse radar types, with mod…
Multi-camera systems hold considerable promise for enhancing visual odometry by expanding the field of view, yet simply adding more cameras does not guarantee higher accuracy. Because increasing the number of cameras also raises the likelihood of degraded or misaligned views, appropriate handling is…
Despite the inherent advantages of thermal infrared(TIR) imaging, large-scale data collection and annotation remain a major bottleneck for TIR-based perception. A practical alternative is to synthesize pseudo TIR data via image translation; however, most RGB-to-TIR approaches heavily rely on RGB-cen
Reliable localization is crucial for navigation in forests, where GPS is often degraded and LiDAR measurements are repetitive, occluded, and structurally complex. These conditions weaken the assumptions of traditional urban-centric localization methods, which assume that consistent features arise fr…
X-band radar serves as the primary sensor on maritime vessels, however, its application in autonomous navigation has been limited due to low sensor resolution and insufficient information content. To enable X-band radar-only autonomous navigation in maritime environments, this paper proposes a place…
Understanding the 3D geometry of transparent objects from RGB images is challenging due to their inherent physical properties, such as reflection and refraction. To address these difficulties, especially in scenarios with sparse views and dynamic environments, we introduce TRAN-D, a novel 2D Gaussia…
Lifelong mapping is crucial for the long-term deployment of robots in dynamic environments. In this paper, we present ELite, an ephemerality-aided LiDAR-based lifelong mapping framework which can seamlessly align multiple session data, remove dynamic objects, and update maps in an end-toend fashion.
Recently, gravity has been highlighted as a crucial constraint for state estimation to alleviate potential vertical drift. Existing online gravity estimation methods rely on pose estimation combined with IMU measurements, which is considered best practice when direct velocity measurements are unavai
Radar ensures robust sensing capabilities in adverse weather conditions, yet challenges remain due to its high inherent noise level. Existing radar odometry has overcome these challenges with strategies such as filtering spurious points, exploiting Doppler velocity, or integrating with inertial meas
Recently, radars have been widely featured in robotics for their robustness in challenging weather conditions. Two commonly used radar types are spinning radars and phased-array radars, each offering distinct sensor characteristics. Existing datasets typically feature only a single type of radar, le
LiDAR place recognition is a crucial module in localization that matches the current location with previously observed environments. Most existing approaches in LiDAR place recognition dominantly focus on the spinning type LiDAR to exploit its large FOV for matching. However, with the recent emergen
LiDAR Place Recognition (LPR) is a key component in robotic localization, enabling robots to align current scans with prior maps of their environment. While Visual Place Recognition (VPR) has embraced Vision Foundation Models (VFMs) to enhance descriptor robustness, LPR has relied on task-specific m…
Building an online 3D LiDAR mapping system that produces a detailed surface reconstruction while remaining computationally efficient is a challenging task. In this paper, we present PlanarMesh, a novel incremental, mesh-based LiDAR reconstruction system that adaptively adjusts mesh resolution to ach
Affine Grassmannian has been favored for expressing proximity between lines and planes due to its theoretical exactness in measuring distances among features. Despite this advantage, the existing method can only measure the proximity without yielding the distance as an explicit function of rigid bod…
Transparent object manipulation remains a significant challenge in robotics due to the difficulty of acquiring accurate and dense depth measurements. Conventional depth sensors often fail with transparent objects, resulting in incomplete or erroneous depth data. Existing depth completion methods str
X-band radar serves as the primary sensor on maritime vessels, however, its application in autonomous navigation has been limited due to low sensor resolution and insufficient information content. To enable X-band radar-only autonomous navigation in maritime environments, this paper proposes a place
Robust and accurate localization in challenging environments is becoming crucial for SLAM. In this paper, we propose a unique sensor configuration for precise and robust odometry by integrating chip radar and a legged robot. Specifically, we introduce a tightly coupled radar-leg odometry algorithm f
Thermal infrared (TIR) cameras are emerging as promising sensors in safety-related fields due to their robustness against external illumination. However, RAW TIR image has 14 bits of pixel depth and needs to be rescaled into 8 bits for general applications. Previous works utilize a global 1D look-up
Maritime radars are prevalently adopted to capture the vessel's omnidirectional data as imagery. Nevertheless, inherent challenges persist with marine radars, including limited frequency, suboptimal resolution, and indeterminate detections. Additionally, the scarcity of discernible landmarks in the
RGB-D cameras are crucial in robotic perception, given their ability to produce images augmented with depth data. However, their limited field of view (FOV) often requires multiple cameras to cover a broader area. In multi-camera RGB-D setups, the goal is typically to reduce camera overlap, optimizi…
Thermal Infrared (TIR) imaging provides robust perception for navigating in challenging outdoor environments but faces issues with poor texture and low image contrast due to its 14/16-bit format. Conventional methods utilize various tone-mapping methods to enhance contrast and photometric consistenc
In the literature points and conics have been major features for camera geometric calibration. Although conics are more informative features than points the loss of the conic property under distortion has critically limited the utility of conic features in camera calibration. Many existing approache…
6D object pose estimation aims to infer the relative pose between the object and the camera using a single image or multiple images. Most works have focused on predicting the object pose without associated uncertainty under occlusion and structural ambiguity (symmetricity). However, these works dema
In recent years, multiple Light Detection and Ranging (LiDAR) systems have grown in popularity due to their enhanced accuracy and stability from the increased field of view (FOV). However, integrating multiple LiDARs can be challenging, attributable to temporal and spatial discrepancies. Common prac
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 mod…
Due to the robustness in sensing, radar has been highlighted, overcoming harsh weather conditions such as fog and heavy snow. In this paper, we present a novel radar-only place recognition that measures the similarity score by utilizing Radon-transformed sinogram images and cross-correlation in freq…
Long-term 3D map management is a fundamental capability required by a robot to reliably navigate in the non-stationary real-world. This paper develops open-source, modular, and readily available LiDAR-based lifelong mapping for urban sites. This is achieved by dividing the problem into successive su…
This paper introduces a stereo thermal camera dataset (STheReO) with multiple navigation sensors to encourage thermal SLAM researches. A thermal camera measures infrared rays beyond the visible spectrum therefore it could provide a simple yet robust solution to visually degraded environments where e…
The awareness for preserving privacy in in-home monitoring robots is increasing. Although several studies have proposed privacy-preserved in-home monitoring robot systems for adults, only a limited amount of attention has been paid attention to research on privacy-preserved in-home monitoring of bab…
In this letter, 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 sens
Achieving general 3D motion estimation for all-visibility has been a key challenge in robotics, especially in extreme environments. The widely adopted camera and LiDAR-based motion estimation critically deteriorate under fog or smoke. In this letter, we devised a unique sensor system for 3D velocity
Overcoming illumination variance is a critical factor in vision-based navigation. Existing methods tackled this radical illumination variance issue by proposing camera control or high dynamic range (HDR) image fusion. Despite these efforts, we have found that the vision-based approaches still suffer
Event cameras are neuromorphic vision sensors that are able to capture high dynamic range with low latency in microseconds, without motion blur. Their strength lies in the unique representation of data as asynchronous events, enabling detection of scene structures less invariantly from dynamic lumin…
Along with feature points for image matching, line features provide additional constraints to solve visual geometric problems in robotics and computer vision (CV). Although recent convolutional neural network (CNN)-based line descriptors are promising for viewpoint changes or dynamic environments, w
Concurrent mapping necessitates data association among vehicles to overcome temporal and sensor modality differences. In this work, we focus on an underwater multi-vehicle mapping scenario in which vehicles have various sensor modalities, namely sonar and camera. This inter-session sonar-optical ima…
Incorporating multi-sensor, in filter-based as well as graph-based simultaneous localization and mapping (SLAM), relies on the uncertainties involved in each measurement. Proper covariance estimation is thus critical to balance confidence levels among sensors. Despite its importance, traditional cov
Estimating a dense and accurate depth map is the key requirement for autonomous driving and robotics. Recent advances in deep learning have allowed depth estimation in full resolution from a single image. Despite this impressive result, many deep-learning-based monocular depth estimation (MDE) algor…
In this letter, we propose a method for accurately estimating the 6-Degree Of Freedom (DOF) pose in an urban environment when a High Definition (HD) map is available. An HD map expresses 3D geometric data with semantic information in a compressed format and thus is more memory-efficient than point c
This paper introduces a multimodal range dataset namely for radio detection and ranging (radar) and light detection and ranging (LiDAR) specifically targeting the urban environment. By extending our workshop paper [1] to a larger scale, this dataset focuses on the range sensor-based place recognitio…
Recent studies in radar-based navigation present promising navigation performance using scanning radars. These scanning radar-based odometry methods are mostly feature-based; they detect and match salient features within a radar image. Differing from existing feature-based methods, this paper report…
In this letter, we introduce a rotational primitive prediction based 6D object pose estimation using a single image as an input. We solve for the 6D object pose of a known object relative to the camera using a single image with occlusion. Many recent state-of-the-art (SOTA) two-step approaches have
We present a novel static point cloud map construction algorithm, called Removert, for use within dynamic urban environments. Leaving only static points and excluding dynamic objects is a critical problem in various robust robot missions in changing outdoors, and the procedure commonly contains comp…
Learning-based ego-motion estimation approaches have recently drawn strong interest from researchers, mostly focusing on visual perception. A few learning-based approaches using Light Detection and Ranging (LiDAR) have been re-ported; however, they heavily rely on a supervised learning manner. Despi…
In this letter, we present a long-term localization method that effectively exploits the structural information of an environment via an image format. The proposed method presents a robust year-round localization performance even when learned in just a single day. The proposed localizer learns a poi
This paper presents a localization and mapping algorithm that leverages a radar system in low-visibility environments. We aim to address disaster situations in which prior knowledge of a place is available from CAD or light detection and ranging (LiDAR) maps, but incoming visibility is severely limi…
In this letter, we propose a thermal-infrared simultaneous localization and mapping (SLAM) system enhanced by sparse depth measurements from light detection ranging (LiDAR). Thermal-infrared cameras are relatively robust against fog, smoke, and dynamic lighting conditions compared to RGB cameras ope
This letter presents a framework for the target-less extrinsic calibration of stereo cameras and light detection and ranging (LiDAR) sensors with a non-overlapping field of view (FOV). In order to solve extrinsic calibration problems under such challenging configurations, the proposed solution explo
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 captu…
This paper describes a framework for direct visual simultaneous localization and mapping (SLAM) combining a monocular camera with sparse depth information from Light Detection and Ranging (LiDAR). To ensure realtime performance while maintaining high accuracy in motion estimation, we present (i) a s…
Under- and oversaturation can cause severe image degradation in many vision-based robotic applications. To control camera exposure in dynamic lighting conditions, we introduce a novel metric for image information measure. Measuring an image gradient is typical when evaluating its level of image deta…
This paper presents a fast single image enhancement that is applicable regardless of channels in various environments. The main idea of the paper is combining model-based and fusion-based dehazing methods, thereby presenting balanced image enhancement while elaborating image details. The proposed me
Compared to diverse feature detectors and descriptors used for visual scenes, describing a place using structural information is relatively less reported. Recent advances in simultaneous localization and mapping (SLAM) provides dense 3D maps of the environment and the localization is proposed by div…
As simultaneous localization and mapping (SLAM) techniques have flourished with the advent of 3D Light Detection and Ranging (LiDAR) sensors, accurate 3D maps are readily available. Many researchers turn their attention to localization in a previously acquired 3D map. In this paper, we propose a nov…
This letter presents an effective solution for 3D urban map generation using aerial images as prior information for Simultaneous Localization and Mapping (SLAM). The research targets urban canyons where Global Positioning System (GPS) signals are highly sporadic and erroneous. As a solution for urba
Researchers in the simultaneous localization and mapping (SLAM) community have taken for granted that uncertainty associated with the robot pose increases until the loop is closed. However, recently identified by [1], the monotonicity of uncertainty during exploration breaks when the robot returns t…
This paper presents a real-time visibility enhancement algorithm for effective underwater visual simultaneous localization and mapping (SLAM). Unlike an aerial environment, an underwater environment contains larger particles and is dominated by a different image degradation model. Our method starts…
In this paper, we present a unified approach for Expectation Maximization (EM) based motion segmentation, estimation and analysis from dense point cloud data. When identifying an underlying motion, literature mainly focuses on three related topics: motion segmentation, estimation and analysis. These…