Sung-Eui Yoon

AAAI Conference 2026 Conference Paper

Towards Test-time Efficient Visual Place Recognition via Asymmetric Query Processing

• Jaeyoon Kim
• Yoonki Cho
• Sung-Eui Yoon

Visual Place Recognition (VPR) has advanced significantly with high-capacity foundation models like DINOv2, achieving remarkable performance. Nonetheless, their substantial computational cost makes deployment on resource-constrained devices impractical. In this paper, we introduce an efficient asymmetric VPR framework that incorporates a high-capacity gallery model for offline feature extraction with a lightweight query network for online processing. A key challenge in this setting is ensuring compatibility between these heterogeneous networks, which conventional approaches address through computationally expensive k-NN-based compatible training. To overcome this, we propose a geographical memory bank that structures gallery features using geolocation metadata inherent in VPR databases, eliminating the need for exhaustive k-NN computations. Additionally, we introduce an implicit embedding augmentation technique that enhances the query network to model feature variations despite its limited capacity. Extensive experiments demonstrate that our method not only significantly reduces computational costs but also outperforms existing asymmetric retrieval techniques, establishing a new aspect for VPR in resource-limited environments.

ICLR Conference 2025 Conference Paper

AdvPaint: Protecting Images from Inpainting Manipulation via Adversarial Attention Disruption

• Joonsung Jeon
• Woo Jae Kim
• Suhyeon Ha
• Sooel Son
• Sung-Eui Yoon

The outstanding capability of diffusion models in generating high-quality images poses significant threats when misused by adversaries. In particular, we assume malicious adversaries exploiting diffusion models for inpainting tasks, such as replacing a specific region with a celebrity. While existing methods for protecting images from manipulation in diffusion-based generative models have primarily focused on image-to-image and text-to-image tasks, the challenge of preventing unauthorized inpainting has been rarely addressed, often resulting in suboptimal protection performance. To mitigate inpainting abuses, we propose ADVPAINT, a novel defensive framework that generates adversarial perturbations that effectively disrupt the adversary’s inpainting tasks. ADVPAINT targets the self- and cross-attention blocks in a target diffusion inpainting model to distract semantic understanding and prompt interactions during image generation. ADVPAINT also employs a two-stage perturbation strategy, dividing the perturbation region based on an enlarged bounding box around the object, enhancing robustness across diverse masks of varying shapes and sizes. Our experimental results demonstrate that ADVPAINT’s perturbations are highly effective in disrupting the adversary’s inpainting tasks, outperforming existing methods; ADVPAINT attains over a 100-point increase in FID and substantial decreases in precision.

IROS Conference 2025 Conference Paper

Efficient Navigation Among Movable Obstacles using a Mobile Manipulator via Hierarchical Policy Learning

• Taegeun Yang
• Jiwoo Hwang
• Jeil Jeong
• Minsung Yoon
• Sung-Eui Yoon

We propose a hierarchical reinforcement learning (HRL) framework for efficient Navigation Among Movable Obstacles (NAMO) using a mobile manipulator. Our approach combines interaction-based obstacle property estimation with structured pushing strategies, facilitating the dynamic manipulation of unforeseen obstacles while adhering to a preplanned global path. The high-level policy generates pushing commands that consider environmental constraints and path-tracking objectives, while the low-level policy precisely and stably executes these commands through coordinated whole-body movements. Comprehensive simulation-based experiments demonstrate improvements in performing NAMO tasks, including higher success rates, shortened traversed path length, and reduced goal-reaching times, compared to baselines. Additionally, ablation studies assess the efficacy of each component, while a qualitative analysis further validates the accuracy and reliability of the real-time obstacle property estimation.

ICRA Conference 2025 Conference Paper

Enhancing Navigation Efficiency of Quadruped Robots via Leveraging Personal Transportation Platforms

• Minsung Yoon
• Sung-Eui Yoon

Quadruped robots face limitations in long-range navigation efficiency due to their reliance on legs. To ameliorate the limitations, we introduce a Reinforcement Learning-based Active Transporter Riding method (RL-ATR), inspired by humans' utilization of personal transporters, including Segways. The RL-ATR features a transporter riding policy and two state estimators. The policy devises adequate maneuvering strategies according to transporter-specific control dynamics, while the estimators resolve sensor ambiguities in non-inertial frames by inferring unobservable robot and transporter states. Comprehensive evaluations in simulation validate proficient command tracking abilities across various transporter-robot models and reduced energy consumption compared to legged locomotion. Moreover, we conduct ablation studies to quantify individual component contributions within the RL-ATR. This riding ability could broaden the locomotion modalities of quadruped robots, potentially expanding the operational range and efficiency.

NeurIPS Conference 2024 Conference Paper

Generalizable Person Re-identification via Balancing Alignment and Uniformity

• Yoonki Cho
• Jaeyoon Kim
• Woo J. Kim
• Junsik Jung
• Sung-Eui Yoon

Domain generalizable person re-identification (DG re-ID) aims to learn discriminative representations that are robust to distributional shifts. While data augmentation is a straightforward solution to improve generalization, certain augmentations exhibit a polarized effect in this task, enhancing in-distribution performance while deteriorating out-of-distribution performance. In this paper, we investigate this phenomenon and reveal that it leads to sparse representation spaces with reduced uniformity. To address this issue, we propose a novel framework, Balancing Alignment and Uniformity (BAU), which effectively mitigates this effect by maintaining a balance between alignment and uniformity. Specifically, BAU incorporates alignment and uniformity losses applied to both original and augmented images and integrates a weighting strategy to assess the reliability of augmented samples, further improving the alignment loss. Additionally, we introduce a domain-specific uniformity loss that promotes uniformity within each source domain, thereby enhancing the learning of domain-invariant features. Extensive experimental results demonstrate that BAU effectively exploits the advantages of data augmentation, which previous studies could not fully utilize, and achieves state-of-the-art performance without requiring complex training procedures. The code is available at https: //github. com/yoonkicho/BAU.

IROS Conference 2024 Conference Paper

Learning-based Adaptive Control of Quadruped Robots for Active Stabilization on Moving Platforms

• Minsung Yoon
• Heechan Shin
• Jeil Jeong
• Sung-Eui Yoon

A quadruped robot faces balancing challenges on a six-degrees-of-freedom moving platform, like subways, buses, airplanes, and yachts, due to independent platform motions and resultant diverse inertia forces on the robot. To alleviate these challenges, we present the Learning-based Active Stabilization on Moving Platforms (LAS-MP), featuring a self-balancing policy and system state estimators. The policy adaptively adjusts the robot’s posture in response to the platform’s motion. The estimators infer robot and platform states based on proprioceptive sensor data. For a systematic training scheme across various platform motions, we introduce platform trajectory generation and scheduling methods. Our evaluation demonstrates superior balancing performance across multiple metrics compared to three baselines. Furthermore, we conduct a detailed analysis of the LAS-MP, including ablation studies and evaluation of the estimators, to validate the effectiveness of each component.

IROS Conference 2024 Conference Paper

LiDAR-camera Online Calibration by Representing Local Feature and Global Spatial Context

• SeongJoo Moon
• Sebin Lee
• Dong He
• Sung-Eui Yoon

LiDAR-camera calibration plays a crucial role in autonomous driving. However, operation-induced factors such as physical vibrations and temperature variations degrade the pre-deployment calibration accuracy, leading to the environmental perception performance deterioration. Recent recalibration methods have achieved online calibration without a target board by leveraging the relative attributes of LiDAR and camera. Nevertheless, we proposes a novel framework for LiDAR-camera online calibration which employs a Transformer network to learn crucial interactions between cameras and LiDAR sensors. Additionally, our novel framework design enables the effective calibration by utilizing correspondence point information between the two sensors. This allows the utilization of global spatial context and achieves high performance by integrating information across modalities. Experimental results indicate that our method demonstrates superior performance compared to state-of-the-art benchmarks.

EAAI Journal 2024 Journal Article

PISD: A linear complexity distance beats dynamic time warping on time series classification and clustering

• Minh-Tuan Tran
• Xuan-May Le
• Van-Nam Huynh
• Sung-Eui Yoon

ICRA Conference 2023 Conference Paper

Learning-based Initialization of Trajectory Optimization for Path-following Problems of Redundant Manipulators

• Minsung Yoon
• Mincheul Kang
• Daehyung Park
• Sung-Eui Yoon

Trajectory optimization (TO) is an efficient tool to generate a redundant manipulator's joint trajectory following a 6-dimensional Cartesian path. The optimization performance largely depends on the quality of initial trajectories. However, the selection of a high-quality initial trajectory is non-trivial and requires a considerable time budget due to the extremely large space of the solution trajectories and the lack of prior knowledge about task constraints in configuration space. To alleviate the issue, we present a learning-based initial trajectory generation method that generates high-quality initial trajectories in a short time budget by adopting example-guided reinforcement learning. In addition, we suggest a null-space projected imitation reward to consider null-space constraints by efficiently learning kinematically feasible motion captured in expert demonstrations. Our statistical evaluation in simulation shows the improved optimality, efficiency, and applicability of TO when we plug in our method's output, compared with three other baselines. We also show the performance improvement and feasibility via real-world experiments with a seven-degree-of-freedom manipulator.

NeurIPS Conference 2023 Conference Paper

Topological RANSAC for instance verification and retrieval without fine-tuning

• Guoyuan An
• Ju-hyeong Seon
• Inkyu An
• Yuchi Huo
• Sung-Eui Yoon

This paper presents an innovative approach to enhancing explainable image retrieval, particularly in situations where a fine-tuning set is unavailable. The widely-used SPatial verification (SP) method, despite its efficacy, relies on a spatial model and the hypothesis-testing strategy for instance recognition, leading to inherent limitations, including the assumption of planar structures and neglect of topological relations among features. To address these shortcomings, we introduce a pioneering technique that replaces the spatial model with a topological one within the RANSAC process. We propose bio-inspired saccade and fovea functions to verify the topological consistency among features, effectively circumventing the issues associated with SP's spatial model. Our experimental results demonstrate that our method significantly outperforms SP, achieving state-of-the-art performance in non-fine-tuning retrieval. Furthermore, our approach can enhance performance when used in conjunction with fine-tuned features. Importantly, our method retains high explainability and is lightweight, offering a practical and adaptable solution for a variety of real-world applications.

ICRA Conference 2023 Conference Paper

Towards Safe Remote Manipulation: User Command Adjustment based on Risk Prediction for Dynamic Obstacles

• Mincheul Kang
• Minsung Yoon
• Sung-Eui Yoon

Real-time remote manipulation requires careful operations by a user to ensure the safety of a robot, which is designed to follow user's commands, against dynamic obstacles. However, a user may give commands to a robot at the risk of collision with dynamic obstacles due to a user's unfamiliar control ability or unexpected situations. In this paper, we propose a risk-aware user command adjustment method to avoid potential collision with dynamic obstacles. Our method consists of a network that predicts the risk of dynamic obstacles and another network that synthesizes commands to avoid obstacles. Based on the predicted risk, our method decides an adjusted command between a user command and a command to avoid collisions. We evaluate our method in problems that face collisions with dynamic obstacles when following given commands and in problems with static obstacles. We show that our method improves safety against the risk of dynamic obstacles or follows user commands when there is no risk. We also demonstrate the feasibility of our method using the real fetch manipulator with seven-degrees-of-freedom.

ICRA Conference 2022 Conference Paper

Confidence-Based Robot Navigation Under Sensor Occlusion with Deep Reinforcement Learning

• Hyeongyeol Ryu
• Minsung Yoon
• Daehyung Park
• Sung-Eui Yoon

This paper considers the problem of prolonged occlusions on navigation sensors due to dust, smudges, soils, etc. Such uncontrollable occlusions often cause lower visibility as well as higher uncertainty that require considerably sophisticated behavior. To secure visibility (i. e. , confidence about the world), we propose a confidence-based navigation method that encourages the robot to explore the uncertain region around the robot maximizing its local confidence. To effectively extract features from the variable size of sensor occlusions, we adopt a point-cloud based representation network. Our method returns a resilient navigation policy via deep reinforcement learning, autonomously avoiding collisions under sensor occlusions while reaching a goal. We evaluate our method in simulated and real-world environments with either static or dynamic obstacles under various sensor-occlusion scenarios. The experimental result shows that our method outperforms baseline methods under the highly occurring sensor occlusion, and achieves maximum 90% and 80% success rates in the tested static and dynamic environments, respectively.

ICRA Conference 2022 Conference Paper

Implicit LiDAR Network: LiDAR Super-Resolution via Interpolation Weight Prediction

• Youngsun Kwon
• Minhyuk Sung
• Sung-Eui Yoon

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.

IROS Conference 2021 Conference Paper

Dynamic Humanoid Locomotion Over Rough Terrain With Streamlined Perception-Control Pipeline

• Moonyoung Lee
• Youngsun Kwon
• Sebin Lee
• Jonghun Choe
• Junyong Park 0002
• Hyobin Jeong
• Yujin Heo
• Min-Su Kim 0005

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.

NeurIPS Conference 2021 Conference Paper

Hypergraph Propagation and Community Selection for Objects Retrieval

• Guoyuan An
• Yuchi Huo
• Sung-Eui Yoon

Spatial verification is a crucial technique for particular object retrieval. It utilizes spatial information for the accurate detection of true positive images. However, existing query expansion and diffusion methods cannot efficiently propagate the spatial information in an ordinary graph with scalar edge weights, resulting in low recall or precision. To tackle these problems, we propose a novel hypergraph-based framework that efficiently propagates spatial information in query time and retrieves an object in the database accurately. Additionally, we propose using the image graph's structure information through community selection technique, to measure the accuracy of the initial search result and to provide correct starting points for hypergraph propagation without heavy spatial verification computations. Experiment results on ROxford and RParis show that our method significantly outperforms the existing query expansion and diffusion methods.

IROS Conference 2020 Conference Paper

Adaptive Kernel Inference for Dense and Sharp Occupancy Grids

• Youngsun Kwon
• Bochang Moon
• Sung-Eui Yoon

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.

IROS Conference 2020 Conference Paper

Optimization-based Path Planning for Person Following using Following Field

• Heechan Shin
• Sung-Eui Yoon

Person following is an essential task for a robot to serve a person. In an indoor environment, however, the following task can be failed due to the occlusion of the target by structures, e. g. , walls or pillars. To address this problem, we propose a method that helps the robot follow the target well and rapidly re-detect the target after missing. The proposed method is an optimization-based path planning which uses a Following Field that we propose in this paper. The following field consists of two sub-fields: the repulsion field getting the robot out of the occluded area, and the target attraction field pushing the robot toward the target. We introduce how to construct the fields and how to integrate the field into a path optimization process. We show that our method works properly for following the target well in a maze consisting of various in-door features.

ICRA Conference 2020 Conference Paper

Robust Sound Source Localization considering Similarity of Back-Propagation Signals

• Inkyu An
• Byeongho Jo
• Youngsun Kwon
• Jung-Woo Choi
• Sung-Eui Yoon

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.

IROS Conference 2020 Conference Paper

TORM: Fast and Accurate Trajectory Optimization of Redundant Manipulator given an End-Effector Path

• Mincheul Kang
• Heechan Shin
• Donghyuk Kim
• Sung-Eui Yoon

A redundant manipulator has multiple inverse kinematics solutions per end-effector pose. Accordingly, there can be many trajectories for joints that follow a given end-effector path in the Cartesian space. In this paper, we present a trajectory optimization of a redundant manipulator (TORM) to synthesize a trajectory that follows a given end-effector path accurately, while achieving smoothness and collision-free manipulation. Our method holistically incorporates three desired properties into the trajectory optimization process by integrating the Jacobian-based inverse kinematics solving method and an optimization-based motion planning approach. Specifically, we optimize a trajectory using two-stage gradient descent to reduce potential competition between different properties during the update. To avoid falling into local minima, we iteratively explore different candidate trajectories with our local update. We compare our method with state-of-the-art methods in test scenes including external obstacles and two non-obstacle problems. Our method robustly minimizes the pose error in a progressive manner while satisfying various desirable properties.

ICRA Conference 2019 Conference Paper

Diffraction-Aware Sound Localization for a Non-Line-of-Sight Source

• Inkyu An
• Doheon Lee
• Jung-Woo Choi
• Dinesh Manocha
• Sung-Eui Yoon

We present a novel sound localization algorithm for a non-line-of-sight (NLOS) sound source in indoor environments. Our approach exploits the diffraction properties of sound waves as they bend around a barrier or an obstacle in the scene. We combine a ray tracing-based sound propagation algorithm with a Uniform Theory of Diffraction (UTD) model, which simulate bending effects by placing a virtual sound source on a wedge in the environment. We precompute the wedges of a reconstructed mesh of an indoor scene and use them to generate diffraction acoustic rays to localize the 3D position of the source. Our method identifies the convergence region of those generated acoustic rays as the estimated source position based on a particle filter. We have evaluated our algorithm in multiple scenarios consisting of static and dynamic NLOS sound sources. In our tested cases, our approach can localize a source position with an average accuracy error of 0. 7m, measured by the L2 distance between estimated and actual source locations in a 7m×7m×3m room. Furthermore, we observe 37% to 130% improvement in accuracy over a state-of-the-art localization method that does not model diffraction effects, especially when a sound source is not visible to the robot.

IROS Conference 2019 Conference Paper

Harmonious Sampling for Mobile Manipulation Planning

• Mincheul Kang
• Donghyuk Kim
• Sung-Eui Yoon

Mobile manipulation planning commonly adopts a decoupled approach that performs planning separately on the base and the manipulator. While this approach is fast, it can generate sub-optimal paths. Another direction is a coupled approach jointly adjusting the base and manipulator in a high-dimensional configuration space. This coupled approach addresses sub-optimality and incompleteness of the decoupled approach, but has not been widely used due to its excessive computational overhead. Given this trade-off space, we present a simple, yet effective mobile manipulation sampling method, harmonious sampling, to perform the coupled approach mainly in difficult regions, where we need to simultaneously maneuver the base and the manipulator. Our method identifies such difficult regions through a low-dimensional base space by utilizing a reachability map given the target end-effector pose and narrow passage detected by generalized Voronoi diagram. For the rest of simple regions, we perform sampling mainly on the base configurations with a predefined joint configuration, accelerating the planning process. We compare our method with the decoupled and coupled approaches in six different problems with varying difficulty. Our method shows meaningful improvements experimentally in terms of time to find an initial solution (up to 5. 6 times faster) and final solution cost (up to 17% lower) over the decoupled approach, especially in difficult scenes with narrow space. We also demonstrate these benefits with a real, mobile Hubo robot.

IROS Conference 2019 Conference Paper

Volumetric Tree *: Adaptive Sparse Graph for Effective Exploration of Homotopy Classes

• Donghyuk Kim
• Mincheul Kang
• Sung-Eui Yoon

We present volumetric tree *, a hybridization of sampling-based and optimization-based motion planning. Volumetric tree * constructs an adaptive sparse graph with volumetric vertices, hyper-spheres encoding free configurations, using a sampling-based motion planner for a homotopy exploration. The coarse-grained paths computed on the sparse graph are refined by optimization-based planning during the execution, while exploiting the probabilistic completeness of the sampling- based planning for the initial path generation. We also suggest a dropout technique probabilistically ensuring that the sampling- based planner is capable of identifying all possible homotopies of solution paths. We compare the proposed algorithm against the state-of-the-art planners in both synthetic and practical benchmarks with varying dimensions, and experimentally show the benefit of the proposed algorithm.

ICRA Conference 2018 Conference Paper

Dancing PRM*: Simultaneous Planning of Sampling and Optimization with Configuration Free Space Approximation

• Donghyuk Kim
• Youngsun Kwon
• Sung-Eui Yoon

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.

IROS Conference 2018 Conference Paper

Kinodynamic Comfort Trajectory Planning for Car-Like Robots

• Heechan Shin
• Donghyuk Kim
• Sung-Eui Yoon

As personal autonomous mobility is getting to be more widely adopted, it is more important to consider comfortability of stuffs and persons carried by such mobility. In this work, we define the comfort of a trajectory as forces, specifically, translational force, received to objects carried by a robot while following the trajectory by measuring impulse. To maximize such a comfort, we propose a novel, kinodynamic comfort path planning method based on our definition of comfort. Our work is based on direct collocation method for handling our nonconvex objective function. We also introduce Bidirectional Obstacle Detection(BOD)that identifies the distances along the perpendicular directions to the trajectory. This is mainly designed for avoiding obstacles while minimizing forces causing discomfort. Our experimental results show that our method can compute trajectories whose comfort measures can be up to 18 times higher than those computed by prior related objectives, e. g. , squared velocity used for generating smooth trajectory.

ICRA Conference 2018 Conference Paper

Reflection-Aware Sound Source Localization

• Inkyu An
• Myung-Bae Son
• Dinesh Manocha
• Sung-Eui Yoon

We present a novel, reflection-aware method for 3D sound localization in indoor environments. Unlike prior approaches, which are mainly based on continuous sound signals from a stationary source, our formulation is designed to localize the position instantaneously from signals within a single frame. We consider direct sound and indirect sound signals that reach the microphones after reflecting off surfaces such as ceilings or walls. We then generate and trace direct and reflected acoustic paths using inverse acoustic ray tracing and utilize these paths with Monte Carlo localization to estimate a 3D sound source position. We have implemented our method on a robot with a cube-shaped microphone array and tested it against different settings with continuous and intermittent sound signals with a stationary or a mobile source. Across different settings, our approach can localize the sound with an average distance error of 0. 8 m tested in a room of 7 m by 7 m area with 3 m height, including a mobile and non-line-of-sight sound source. We also reveal that the modeling of indirect rays increases the localization accuracy by 40% compared to only using direct acoustic rays.

IROS Conference 2016 Conference Paper

Anytime RRBT for handling uncertainty and dynamic objects

• Hyunchul Yang
• Jongwoo Lim
• Sung-Eui Yoon

We present an efficient anytime motion planner for mobile robots that considers both other dynamic obstacles and uncertainty caused by various sensors and low-level controllers. Our planning algorithm, which is an anytime extension of the Rapidly-exploring Random Belief Tree (RRBT), maintains the best possible path throughout the robot execution, and the generated path gets closer to the optimal one as more computation resources are allocated. We propose a branch-and-bound method to cull out unpromising areas by considering path lengths and uncertainty. We also propose an uncertainty-aware velocity obstacle as a simple local analysis to avoid dynamic obstacles efficiently by finding a collision-free velocity. We have tested our method with three benchmarks that have non-linear measurement regions or potential collisions with dynamic obstacles. By using the proposed methods, we achieve up to five times faster performance given a fixed path cost.

ICRA Conference 2016 Conference Paper

Super ray based updates for occupancy maps

• Youngsun Kwon
• Donghyuk Kim
• Sung-Eui Yoon

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.

ICRA Conference 2014 Conference Paper

Cloud RRT ∗: Sampling Cloud based RRT ∗

• Donghyuk Kim
• Junghwan Lee
• Sung-Eui Yoon

We present a novel biased sampling technique, Cloud RRT ∗, for efficiently computing high-quality collision-free paths, while maintaining the asymptotic convergence to the optimal solution. Our method uses sampling cloud for allocating samples on promising regions. Our sampling cloud consists of a set of spheres containing a portion of the C-space. In particular, each sphere projects to a collision-free spherical region in the workspace. We initialize our sampling cloud by conducting a workspace analysis based on the generalized Voronoi graph. We then update our sampling cloud to refine the current best solution, while maintaining the global sampling distribution for exploring understudied other homotopy classes. We have applied our method to a 2D motion planning problem with kinematic constraints, i. e. , the Dubins vehicle model, and compared it against the state-of-the-art methods. We achieve better performance, up to three times, over prior methods in a robust manner.

ICRA Conference 2014 Conference Paper

PROT: Productive regions oriented task space path planning for hyper-redundant manipulators

• Junghwan Lee
• Sung-Eui Yoon

In this paper we propose a novel efficient sampling bias technique to improve the performance of a task space trajectory planner for hyper-redundant manipulators. We defines productive regions in the task space as a set of states that can lead effectively to a goal state. We first compute a maximum reachable area (MRA) where a robot can reach from the node by an employed local planner for a node in the task space. When the MRA of a node contains the goal state, we call it promising and bias our sampling to cover promising MRAs. When the MRA does not contain the goal state, we call it unpromising and construct a detour sampling domain for detouring operations from obstacles constraining the manipulator. The union of promising MRAs and detour sampling domains approximates our productive regions, and we bias our sampling to cover these domains more. We have applied our Productive Regions Oriented Task space planner (PROT) to various types of robots in R 2 task space and achieved up to 3. 54 times improvement over the state-of-the-art task space planner. We have additionally verified the benefits of our method by applying it to cabled mobile robot planning.

IROS Conference 2013 Conference Paper

VLSH: Voronoi-based locality sensitive hashing

• Tieu Lin Loi
• Jae-Pil Heo
• Junghwan Lee
• Sung-Eui Yoon

We present a fast, yet accurate k-nearest neighbor search algorithm for high-dimensional sampling-based motion planners. Our technique is built on top of Locality Sensitive Hashing (LSH), but is extended to support arbitrary distance metrics used for motion planning problems and adapt irregular distributions of samples generated in the configuration space. To enable such novel characteristics our method embeds samples generated in the configuration space into a simple l 2 norm space by using pivot points. We then implicitly define Voronoi regions and use local LSHs with varying quantization factors for those Voronoi regions. We have applied our method and other prior techniques to high-dimensional motion planning problems. Our method is able to show performance improvement by a factor of up to three times even with higher accuracy over prior, approximate nearest neighbor search techniques.

ICRA Conference 2012 Conference Paper

SR-RRT: Selective retraction-based RRT planner

• Junghwan Lee
• OSung Kwon
• Liangjun Zhang
• Sung-Eui Yoon

We present a novel retraction-based planner, selective retraction-based RRT, for efficiently handling a wide variety of environments that have different characteristics. We first present a bridge line-test that can identify regions around narrow passages, and then perform an optimization-based retraction operation selectively only at those regions. We also propose a non-colliding line-test, a dual operator to the bridge line-test, as a culling method to avoid generating samples near wide-open free spaces and thus to generate more samples around narrow passages. These two tests are performed with a small computational overhead and are integrated with a retraction-based RRT. In order to demonstrate benefits of our method, we have tested our method with different benchmarks that have varying amounts of narrow passages. Our method achieves up to 21 times and 3. 5 times performance improvements over a basic RRT and an optimization-based retraction RRT, respectively. Furthermore, our method consistently improves the performances of other tested methods across all the tested benchmarks that have or do not have narrow passages.