arXiv subjects

H. Jin Kim

Publications and source records attributed to H. Jin Kim.

88 records · Page 5Linked to original sources

arXiv2021

Learning a Domain-Agnostic Visual Representation for Autonomous Driving via Contrastive Loss

Deep neural networks have been widely studied in autonomous driving applications such as semantic segmentation or depth estimation. However, training a neural network in a supervised manner requires a large amount of annotated labels which are expensive and time-consuming to collect. Recent studies leverage synthetic data collected from a virtual environment which are much easier to acquire and more accurate compared to data from the real world, but they usually suffer from poor generalization due to the inherent domain shift problem. In this paper, we propose a Domain-Agnostic Contrastive Learning (DACL) which is a two-stage unsupervised domain adaptation framework with cyclic adversarial training and contrastive loss. DACL leads the neural network to learn domain-agnostic representation to overcome performance degradation when there exists a difference between training and test data distribution. Our proposed approach achieves better performance in the monocular depth estimation task compared to previous state-of-the-art methods and also shows effectiveness in the semantic segmentation task.

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arXiv2020

Gaussian RAM: Lightweight Image Classification via Stochastic Retina-Inspired Glimpse and Reinforcement Learning

Previous studies on image classification have mainly focused on the performance of the networks, not on real-time operation or model compression. We propose a Gaussian Deep Recurrent visual Attention Model (GDRAM)- a reinforcement learning based lightweight deep neural network for large scale image classification that outperforms the conventional CNN (Convolutional Neural Network) which uses the entire image as input. Highly inspired by the biological visual recognition process, our model mimics the stochastic location of the retina with Gaussian distribution. We evaluate the model on Large cluttered MNIST, Large CIFAR-10 and Large CIFAR-100 datasets which are resized to 128 in both width and height.

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arXiv2020

Pose Correction Algorithm for Relative Frames between Keyframes in SLAM

With the dominance of keyframe-based SLAM in the field of robotics, the relative frame poses between keyframes have typically been sacrificed for a faster algorithm to achieve online applications. However, those approaches can become insufficient for applications that may require refined poses of all frames, not just keyframes which are relatively sparse compared to all input frames. This paper proposes a novel algorithm to correct the relative frames between keyframes after the keyframes have been updated by a back-end optimization process. The correction model is derived using conservation of the measurement constraint between landmarks and the robot pose. The proposed algorithm is designed to be easily integrable to existing keyframe-based SLAM systems while exhibiting robust and accurate performance superior to existing interpolation methods. The algorithm also requires low computational resources and hence has a minimal burden on the whole SLAM pipeline. We provide the evaluation of the proposed pose correction algorithm in comparison to existing interpolation methods in various vector spaces, and our method has demonstrated excellent accuracy in both KITTI and EuRoC datasets.

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arXiv2020

Moving object detection for visual odometry in a dynamic environment based on occlusion accumulation

Detection of moving objects is an essential capability in dealing with dynamic environments. Most moving object detection algorithms have been designed for color images without depth. For robotic navigation where real-time RGB-D data is often readily available, utilization of the depth information would be beneficial for obstacle recognition. Here, we propose a simple moving object detection algorithm that uses RGB-D images. The proposed algorithm does not require estimating a background model. Instead, it uses an occlusion model which enables us to estimate the camera pose on a background confused with moving objects that dominate the scene. The proposed algorithm allows to separate the moving object detection and visual odometry (VO) so that an arbitrary robust VO method can be employed in a dynamic situation with a combination of moving object detection, whereas other VO algorithms for a dynamic environment are inseparable. In this paper, we use dense visual odometry (DVO) as a VO method with a bi-square regression weight. Experimental results show the segmentation accuracy and the performance improvement of DVO in the situations. We validate our algorithm in public datasets and our dataset which also publicly accessible.

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arXiv2020

Detection-Aware Trajectory Generation for a Drone Cinematographer

This work investigates an efficient trajectory generation for chasing a dynamic target, which incorporates the detectability objective. The proposed method actively guides the motion of a cinematographer drone so that the color of a target is well-distinguished against the colors of the background in the view of the drone. For the objective, we define a measure of color detectability given a chasing path. After computing a discrete path optimized for the metric, we generate a dynamically feasible trajectory. The whole pipeline can be updated on-the-fly to respond to the motion of the target. For the efficient discrete path generation, we construct a directed acyclic graph (DAG) for which a topological sorting can be determined analytically without the depth-first search. The smooth path is obtained in quadratic programming (QP) framework. We validate the enhanced performance of state-of-the-art object detection and tracking algorithms when the camera drone executes the trajectory obtained from the proposed method.

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arXiv2020

Aerial Manipulation using Model Predictive Control for Opening a Hinged Door

Existing studies for environment interaction with an aerial robot have been focused on interaction with static surroundings. However, to fully explore the concept of an aerial manipulation, interaction with moving structures should also be considered. In this paper, a multirotor-based aerial manipulator opening a daily-life moving structure, a hinged door, is presented. In order to address the constrained motion of the structure and to avoid collisions during operation, model predictive control (MPC) is applied to the derived coupled system dynamics between the aerial manipulator and the door involving state constraints. By implementing a constrained version of differential dynamic programming (DDP), MPC can generate position setpoints to the disturbance observer (DOB)-based robust controller in real-time, which is validated by our experimental results.

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arXiv2020

Efficient Multi-Agent Trajectory Planning with Feasibility Guarantee using Relative Bernstein Polynomial

This paper presents a new efficient algorithm which guarantees a solution for a class of multi-agent trajectory planning problems in obstacle-dense environments. Our algorithm combines the advantages of both grid-based and optimization-based approaches, and generates safe, dynamically feasible trajectories without suffering from an erroneous optimization setup such as imposing infeasible collision constraints. We adopt a sequential optimization method with \textit{dummy agents} to improve the scalability of the algorithm, and utilize the convex hull property of Bernstein and relative Bernstein polynomial to replace non-convex collision avoidance constraints to convex ones. The proposed method can compute the trajectory for 64 agents on average 6.36 seconds with Intel Core i7-7700 @ 3.60GHz CPU and 16G RAM, and it reduces more than $50\%$ of the objective cost compared to our previous work. We validate the proposed algorithm through simulation and flight tests.

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arXiv2020

Fail-safe Flight of a Fully-Actuated Quadcopter in a Single Motor Failure

In this paper, we introduce a new quadcopter fail-safe flight solution that can perform the same four controllable degrees-of-freedom flight as a regular multirotor even when a single thruster fails. The new solution employs a novel multirotor platform known as the T3-Multirotor and utilizes a distinctive strategy of actively controlling the center of gravity position to restore the nominal flight performance. A dedicated control structure is introduced, along with a detailed analysis of the dynamic characteristics of the platform that change during emergency flights. Experimental results are provided to validate the feasibility of the proposed fail-safe flight strategy.

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arXiv2019

Integrated Motion Planner for Real-time Aerial Videography with a Drone in a Dense Environment

This letter suggests an integrated approach for a drone (or multirotor) to perform an autonomous videography task in a 3-D obstacle environment by following a moving object. The proposed system includes 1) a target motion prediction module which can be applied to dense environments and 2) a hierarchical chasing planner based on a proposed metric for visibility. In the prediction module, we minimize observation error given that the target object itself does not collide with obstacles. The estimated future trajectory of target is obtained by covariant optimization. The other module, chasing planner, is in a bi-level structure composed of preplanner and smooth planner. In the first phase, we leverage a graph-search method to preplan a chasing corridor which incorporates safety and visibility of target during a time window. In the subsequent phase, we generate a smooth and dynamically feasible path within the corridor using quadratic programming (QP). We validate our approach with multiple complex scenarios and actual experiments. The source code can be found in https://github.com/icsl-Jeon/traj_gen_vis

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arXiv2019

Fast Trajectory Planning for Multiple Quadrotors using Relative Safe Flight Corridor

This paper presents a new trajectory planning method for multiple quadrotors in obstacle-dense environments. We suggest a relative safe flight corridor (RSFC) to model safe region between a pair of agents, and it is used to generate linear constraints for inter-collision avoidance by utilizing the convex hull property of relative Bernstein polynomial. Our approach employs a graph-based multi-agent pathfinding algorithm to generate an initial trajectory, which is used to construct a safe flight corridor (SFC) and RSFC. We express the trajectory as a piecewise Bernstein polynomial and formulate the trajectory planning problem into one quadratic programming problem using linear constraints from SFC and RSFC. The proposed method can compute collision-free trajectory for 16 agents within a second and for 64 agents less than a minute, and it is validated both through simulation and indoor flight test.

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arXiv2019

Robust Translational Force Control of Multi-Rotor UAV for Precise Acceleration Tracking

In this paper, we introduce a translational force control method with disturbance observer (DOB)-based force disturbance cancellation for precise three-dimensional acceleration control of a multi-rotor UAV. The acceleration control of the multi-rotor requires conversion of the desired acceleration signal to the desired roll, pitch, and total thrust. But because the attitude dynamics and the thrust dynamics are different, simple kinematic signal conversion without consideration of those difference can cause serious performance degradation in acceleration tracking. Unlike most existing translational force control techniques that are based on such simple inversion, our new method allows controlling the acceleration of the multi-rotor more precisely by considering the dynamics of the multi-rotor during the kinematic inversion. By combining the DOB with the translational force system that includes the improved conversion technique, we achieve robustness with respect to the external force disturbances that hinders the accurate acceleration control. mu-analysis is performed to ensure the robust stability of the overall closed-loop system, considering the combined effect of various possible model uncertainties. Both simulation and experiment are conducted to validate the proposed technique, which confirms the satisfactory performance to track the desired acceleration of the multi-rotor.

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arXiv2019

Robust Real-time RGB-D Visual Odometry in Dynamic Environments via Rigid Motion Model

In the paper, we propose a robust real-time visual odometry in dynamic environments via rigid-motion model updated by scene flow. The proposed algorithm consists of spatial motion segmentation and temporal motion tracking. The spatial segmentation first generates several motion hypotheses by using a grid-based scene flow and clusters the extracted motion hypotheses, separating objects that move independently of one another. Further, we use a dual-mode motion model to consistently distinguish between the static and dynamic parts in the temporal motion tracking stage. Finally, the proposed algorithm estimates the pose of a camera by taking advantage of the region classified as static parts. In order to evaluate the performance of visual odometry under the existence of dynamic rigid objects, we use self-collected dataset containing RGB-D images and motion capture data for ground-truth. We compare our algorithm with state-of-the-art visual odometry algorithms. The validation results suggest that the proposed algorithm can estimate the pose of a camera robustly and accurately in dynamic environments.

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arXiv2019

Edge Detection for Event Cameras using Intra-pixel-area Events

In this work, we propose an edge detection algorithm by estimating a lifetime of an event produced from dynamic vision sensor (DVS), also known as event camera. The event camera, unlike traditional CMOS camera, generates sparse event data at a pixel whose log-intensity changes. Due to this characteristic, theoretically, there is only one or no event at the specific time, which makes it difficult to grasp the world captured by the camera at a particular moment. In this work, we present an algorithm that keeps the event alive until the corresponding event is generated in a nearby pixel so that the shape of an edge is preserved. Particularly, we consider a pixel area to fit a plane on Surface of Active Events (SAE) and call the point inside the pixel area closest to the plane as a intra-pixel-area event. These intra-pixel-area events help the fitting plane algorithm to estimate life time robustly and precisely. Our algorithm performs better in terms of sharpness and similarity metric than the accumulation of events over fixed counts or time intervals, when compared with the existing edge detection algorithms, both qualitatively and quantitatively.

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arXiv2019

Online Trajectory Generation of a MAV for Chasing a Moving Target in 3D Dense Environments

This work deals with a moving target chasing mission of an aerial vehicle equipped with a vision sensor in a cluttered environment. In contrast to obstacle-free or sparse environments, the chaser should be able to handle collision and occlusion simultaneously with flight efficiency. In order to tackle these challenges with real-time replanning, we introduce a metric for target visibility and propose a cascaded chasing planner. By means of the graph-search methods, we first generate a sequence of chasing corridors and waypoints which ensure safety and optimize visibility. In the following phase, the corridors and waypoints are utilized as constraints and objective in quadratic programming from which we complete a dynamically feasible trajectory for chasing. The proposed algorithm is tested in multiple dense environments. The simulator AutoChaser with full code implementation and GUI can be found in https://github.com/icsl-Jeon/traj_gen_vis

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arXiv2016

Control of an Aerial Manipulator using On-line Parameter Estimator for an Unknown Payload

This paper presents an estimation and control algorithm for an aerial manipulator using a hexacopter with a 2-DOF robotic arm. The unknown parameters of a payload are estimated by an on-line estimator based on parametrization of the aerial manipulator dynamics. With the estimated mass information and the augmented passivity-based controller, the aerial manipulator can fly with the unknown object. Simulation for an aerial manipulator is performed to compare estimation performance between the proposed control algorithm and conventional adaptive sliding mode controller. Experimental results show a successful flight of a custom-made aerial manipulator while the unknown parameters related to an additional payload were estimated satisfactorily.

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arXiv2012

Onboard Flight Control of a Small Quadrotor Using Single Strapdown Optical Flow Sensor

This paper considers onboard control of a small-sized quadrotor using a strapdown embedded optical flow sensor which is conventionally used for desktop mice. The vehicle considered in this paper can carry only few dozen grams of payload, therefore conventional camera-based optical flow methods are not applicable. We present hovering control of the small-sized quadrotor using a single-chip optical flow sensor, implemented on an 8-bit microprocessor without external sensors or communication with a ground control station. Detailed description of all the system components is provided along with evaluation of the accuracy. Experimental results from flight tests are validated with the ground-truth data provided by a high-accuracy reference system.

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