IROS 2019 论文列表

We present a novel data structure for SIMD optimized simultaneous bounding volume hierarchy (BVH) traversals like they appear for instance in collision detection tasks. In contrast to all previous approaches, we consider both the traversal algorithm and the construction of the BVH. The main idea is to increase the branching factor of the BVH according to the available SIMD registers and parallelize the simultaneous BVH traversal using SIMD operations. This requires a novel BVH construction method because traditional BVHs for collision detection usually are simple binary trees. To do that, we present a new BVH construction method based on a clustering algorithm, Batch Neural Gas, that is able to build efficient n-ary tree structures along with SIMD optimized simultaneous BVH traversal. Our results show that our new data structure outperforms binary trees significantly.

Routing of a fleet of automated unit-occupancy vehicles in a capacitated transportation network is an emerging problem that needs to be addressed to realize large-scale automated transportation systems. We adopt an existing network-flow-based model for the problem and present a new reformulation based on Dantzig-Wolfe decomposition. This reformulation allows us to apply the column generation solution technique which, in turn, enables us to solve large-scale problem instances with tens of thousands of requests on networks with thousands of links. We empirically compare our method to the state-of-the-art approach on several standard benchmark instances and find that the computational time of our solution approach scales qualitatively better in all tested problem instance parameters: namely, in the size of the transportation network, in the magnitude of demand intensity, and in the number of demand flows.

We present a direct visual-inertial odometry (VIO) method which estimates the motion of the sensor setup and sparse 3D geometry of the environment based on measurements from a rolling-shutter camera and an inertial measurement unit (IMU). The visual part of the system performs a photometric bundle adjustment on a sparse set of points. This direct approach does not extract feature points and is able to track not only corners, but any pixels with sufficient gradient magnitude. Neglecting rolling-shutter effects in the visual part severely degrades accuracy and robustness of the system. In this paper, we incorporate a rolling-shutter model into the photometric bundle adjustment that estimates a set of recent keyframe poses and the inverse depth of a sparse set of points. IMU information is accumulated between several frames using measurement preintegration, and is inserted into the optimization as an additional constraint between selected keyframes. For every keyframe we estimate not only the pose but also velocity and biases to correct the IMU measurements. Unlike systems with global-shutter cameras, we use both IMU measurements and rolling-shutter effects of the camera to estimate velocity and biases for every state. Last, we evaluate our system on a new dataset that contains global-shutter and rolling-shutter images, IMU data and ground-truth poses for ten different sequences, which we make publicly available. Evaluation shows that the proposed method outperforms a system where rolling shutter is not modelled and achieves similar accuracy to the global-shutter method on global-shutter data.

In this paper, we present an approach to generate robot motions for robust parts assembly. The computation of motions for parts assembly usually requires an exact model of all relevant objects. Generating detailed object models, including friction and dynamics, is often complex and time-consuming, especially in the context of elastic parts. In addition, executing motions on real hardware will usually introduce further uncertainty. For this reason, we propose an approach that is inherently robust against model parameter uncertainties and unknown characteristics of elastic parts. Our planner explicitly takes into account the internal states of articulated objects, as well as uncertain model parameters, by constructing a search tree in the belief-parameter-space. It yields successful assembly motions from coarse object models and thus eliminates the need for detailed parameter tuning. We evaluated our approach with respect to four assembly tasks. Extensive simulations show that our planner significantly increases the success-rate compared to previous approaches. Numerous experiments on a real robot confirm the simulated results.

In this paper, we present a robust and efficient estimation approach with multi-camera, odometer and gyroscope. Robust initialization, tightly-coupled optimization estimator and multi-camera loop-closure detection are utilized in the proposed approach. In initialization, the measurements of odometer and gyroscope are used to compute scale, and then estimate the bias of sensors. In estimator, the pre-integration of odometer and gyroscope is derived and combined with the measurements of multi-camera to estimate the motion in a tightly-coupled optimization framework. In loop-closure detection, a connection between different cameras of the vehicle can be built, which significantly improve the success rate of loop-closure detection. The proposed algorithm is validated in multiple real-world datasets collected in different places, time, weather and illumination. Experimental results show that the proposed approach can estimate the motion of vehicles robustly and efficiently.

Small-sized unmanned aerial vehicles (UAVs) have been widely investigated for use in a variety of applications such as remote sensing and aerial surveying. Direct three-dimensional (3D) mapping using a small-sized UAV equipped with a laser scanner is required for numerous remote sensing applications. In direct 3D mapping, the precise information about the position and attitude of the UAV is necessary for constructing 3D maps. In this study, we propose a novel and robust technique for estimating the position and attitude of small-sized UAVs by employing multiple low-cost and light-weight global navigation satellite system (GNSS) antennas/receivers. Using the “redundancy'' of multiple GNSS receivers, we enhance the performance of real-time kinematic (RTK)-GNSS by employing single-frequency GNSS receivers. This method consists of two approaches: hybrid GNSS fix solutions and consistency examination of the GNSS signal strength. The fix rate of RTK-GNSS using single-frequency GNSS receivers can be highly enhanced to combine multiple RTK-GNSS to fix solutions in the multiple antennas. In addition, positioning accuracy and fix rate can be further enhanced to detect multipath signals by using multiple GNSS antennas. In this study, we developed a prototype UAV that is equipped with six GNSS antennas /receivers. From the static test results, we conclude that the proposed technique can enhance the accuracy of the position and attitude estimation in multipath environments. From the flight test, the proposed system could generate a 3D map with an accuracy of 5 cm.

Ensuring safety in trajectory planning of multirotor systems is an essential element for risk-free operation. Even if the generated trajectory is known to be safe in the planning phase, unknown disturbance during an actual operation can lead to a dangerous situation. This paper proposes safety-guaranteed receding horizon planning against unknown, but bounded, disturbances. We first characterize forward reachable set (FRS) of the system, the set of states after a certain duration considering all possible disturbances, using Hamilton-Jacobi (HJ) reachability analysis. To compute the FRSs in real-time, we conservatively approximate the true FRS and perform ellipsoidal parameterization on the FRSs. Using the FRSs, we can plan a robust trajectory that avoids risky regions and rapidly re-plan the trajectory when the system encounters sudden disturbance. The proposed method is validated through an experiment of avoiding obstacles in a wind.

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.

In outdoor scenarios changing conditions (e.g., seasonal, weather and lighting effects) have a substantial impact on the appearance of a scene, which often prevents successful visual localization. The application of an unsupervised Slow Feature Analysis (SFA) on the images captured by an autonomous robot enables self-localization from a single image. However, changes occurring during the training phase or over a more extended period can affect the learned representations. To address the problem, we propose to join long-term recordings from an outdoor environment based on their position correspondences. The established hierarchical model trained on raw images performs well, but as an extension, we extract Fourier components of the views and use that for learning of spatial representations, which reduces the computation time and makes it adequate to run on an ARM embedded system. We present the experimental results from a simulated environment and real-world outdoor recordings collected over a full year, which has effects like different day time, weather, seasons and dynamic objects. Results show an increasing invariance w.r.t. changing conditions over time, thus an outdoor robot can improve its localization performance during operation.

Non-rigid point set registration (PSR) is an outstanding and fundamental problem in fields of robotics, computer vision, medical image analysis and imageguided surgery. The aim of a non-rigid registration problem is to align together two point sets that have been deformed. We have derived and presented a novel registration algorithm that non-rigidly registers two point sets together. The assumption of isotropic localization error is shared in the previous non-rigid registration algorithms. In this paper, the position localization error is generalized to the anisotropic cases, which means that the error distribution is not the same in different spatial directions. The motivation of considering the anisotropic characteristic is that the point localization error is actually different in three spatial directions in real applications. Mathematically, the difficulty in dealing with the anisotropic error case comes from the change from a standard deviation that is a scalar to a covariance matrix. The formulas for updating the parameters in both expectation and maximization steps are derived. In the expectation step, we compute the posterior probabilities that represent the correspondences between points in two PSs. In the maximization step, given the current posteriors, the covariance matrix of the position localization error and the non-rigid transformation are updated. To facilitate the proposed algorithm, the low-rank approximation variation of our method is also presented. We have demonstrated through experiments that the proposed algorithm outperforms the state-of-the-art ones in terms of registration and accuracy and robustness to noise. More specifically, most of the experimental results have passed the statistical tests at the 5% significance level.

Loop closure detection (LCD) is a crucial technique for robots, which can correct accumulated localization errors after long time explorations. In this paper, we propose a robust LCD algorithm based on Bag of SuperPoints and graph verification. The system first extracts interest points and feature descriptors using the SuperPoint neural network. Then a visual vocabulary is trained in an incremental and self-supervised manner considering the relations between consecutive training images. Finally, a topological graph is constructed using matched feature points to verify candidate loop closures obtained by a Bag-of-Words (BoW) framework. Comparative experiments with state-of-the-art LCD algorithms on several typical datasets have been carried out. The results demonstrate that our proposed graph verification method can significantly improve the accuracy of image matching and the overall LCD approach outperforms existing methods.

This paper presents a robust impedance shaping controller for teleoperation systems. An integral sliding mode control law (ISM) is employed together with standard robot inverse dynamics to reject disturbances and uncertainties acting on the robot model and obtain an ideal fully-decoupled system. Higher level optimization-based controllers are responsible for enforcing the desired end effector impedance on master and slave manipulators, as well as for solving possible kinematic redundancies and satisfying control constraints. A three-plus-one channel teleoperation architecture is proposed, with an in-depth analysis of its stability and transparency properties in presence of variable communication delays, based on Llewellyn' s absolute stability theorem. Impedance parameters tuning criteria are derived and the proposed scheme performance is compared in simulation with a time-domain passivity approach. The validation of the proposed controller is carried out on a ABB YuMi dual-arm redundant robot, with one arm employed as a master and the other one as a slave device.

In recent years, many excellent works on visual-inertial SLAM and laser-based SLAM have been proposed. Although inertial measurement unit (IMU) significantly improve the motion estimate performance by reducing the impact of illumination variation or texture-less region on visual tracking, tracking failures occur when in such an environment for a long time. Similarly, when in structure-less environments, laser module will fail since lack of sufficient geometric features. Besides, motion estimation by moving lidar has the problem of distortion since range measurements are received continuously. To solve these problems, we propose a robust and high-accuracy visual-inertial-laser SLAM system. The system starts with a visual-inertial tightly-coupled method for motion estimation, followed by scan matching to further optimize the estimation and register point cloud on the map. Furthermore, we enable modules to be adjusted automatically and flexibly. That is, when one of these modules fails, the remaining modules will undertake the motion-tracking task. For further improving the accuracy, loop closure and proximity detection are implemented to eliminate drift accumulation. When loop or proximity is detected, we perform six degree-of-freedom (6-DOF) pose graph optimization to achieve the global consistency. The performance of our system is verified on public dataset, and the experimental results show that the proposed method achieves superior accuracy against other state-of-the-art algorithms.

This paper addresses the problem of estimating the Euclidean transformation relating two rigidly attached reference frames from motion measurements: a problem that is commonly referred to as the Hand-Eye calibration. The motion measurements are often affected by synchronization issues and hardware malfunctions. When using pose sensors, such as Electromagnetic or Inertial Measurement Units, the measurements are often less reliable than those obtained by a traditional robot-link and camera pair. Corrupt measurements, whether due to disturbances, large synchronization mismatches or malfunctions, are considered outliers that ought to be filtered out of the estimation process. While this may be achieved non-deterministically by using Random Sample Consensus, we propose a deterministic, robust and accurate method for solving the Hand-Eye calibration problem despite the presence of large amounts of outliers and high levels of measurement noise. The proposed method is based on a reformulation of this estimation problem as a rank-constrained semi-definite programming problem allowing for robustness to be enforced via an iteratively re-weighted optimization approach.

We present a method for planning robust grasps over uncertain shape completed objects. For shape completion, a deep neural network is trained to take a partial view of the object as input and outputs the completed shape as a voxel grid. The key part of the network is dropout layers which are enabled not only during training but also at run-time to generate a set of shape samples representing the shape uncertainty through Monte Carlo sampling. Given the set of shape completed objects, we generate grasp candidates on the mean object shape but evaluate them based on their joint performance in terms of analytical grasp metrics on all the shape candidates. We experimentally validate and benchmark our method against another state-of-the-art method with a Barrett hand on 90000 grasps in simulation and 200 grasps on a real Franka Emika Panda. All experimental results show statistically significant improvements both in terms of grasp quality metrics and grasp success rate, demonstrating that planning shape-uncertainty-aware grasps brings significant advantages over solely planning on a single shape estimate, especially when dealing with complex or unknown objects.

Cable manipulation is a challenging task for robots. The major challenge is that cables have high degrees of freedom and are easy to deform during manipulation. In this paper, we propose a novel framework SPR-RWLS to manipulate cables, which includes real-time cable tracking and robust local deformation model approximation. For cable tracking, structure preserved registration (SPR) is utilized to robustly estimate the movement of selected points on a cable even in the presence of sensor noise, outliers, and occlusions. Robust weighted least squares (RWLS) is then applied to calculate the local deformation model of the cable under uncertainties. We show that SPR-RWLS enables the dual-arm robots to manipulate cables with different thicknesses and lengths to different desired curvatures in multiple scenarios. We also show that real-time implementation of the proposed method can be simplified by parallel computation.

Humans often assume that robots are rational. We believe robots take optimal actions given their objective; hence, when we are uncertain about what the robot's objective is, we interpret the robot's actions as optimal with respect to our estimate of its objective. This approach makes sense when robots straightforwardly optimize their objective, and enables humans to learn what the robot is trying to achieve. However, our insight is that-when robots are aware that humans learn by trusting that the robot actions are rational-intelligent robots do not act as the human expects; instead, they take advantage of the human's trust, and exploit this trust to more efficiently optimize their own objective. In this paper, we formally model instances of human-robot interaction (HRI) where the human does not know the robot's objective using a two-player game. We formulate different ways in which the robot can model the uncertain human, and compare solutions of this game when the robot has conservative, optimistic, rational, and trusting human models. In an offline linear-quadratic case study and a real-time user study, we show that trusting human models can naturally lead to communicative robot behavior, which influences end-users and increases their involvement.

Endovascular procedures require real time visual feedback on the location of inserted catheters. This is currently achieved using X-ray fluoroscopy, which causes exposure to radiation. This study describes an alternative method using a robotic ultrasound system for catheter tracking and navigation in endovascular interventions, focusing on endovascular aneurysm repair. This approach relies on the registration of pre-operative images to provide both a tracking trajectory and visual feedback of the real-time catheter position. The procedure was validated on healthy volunteers and on a phantom that included a realistic vessel structure, showing an average tracking error of the moving catheter tip of 1.78±1.02 mm.

In recent years, reinforcement learning has been developed dramatically and is widely used to solve control problems, e.g., playing games. However, there are still some problems for reinforcement learning to perform robotic control tasks. Fortunately, the kernel trick-based methods provide a chance to deal with those challenges. This work aims at developing a kernel trick-based learning control method to carry out robotic tracking control tasks. A reward system, in this work, is presented in order to speed up the learning processes. And then, a kernel trick-based reinforcement learning tracking controller is presented to perform tracking control tasks on a robotic manipulator system. To evaluate the policy and assist the reward system to accelerate the speed of finding the optimal control policy, a critic system is introduced. Finally, from the comparison with the benchmark, the simulation results illustrate that our algorithm has faster convergence rate and can execute tracking control tasks effectively, the reward function and the critic system proposed in this work is efficient.