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

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.N

Networked operation of a UAV using Gaussian process-based delay compensation and model predictive control

This study addresses an operation of unmanned aerial vehicles (UAVs) in a network environment where there is time-varying network delay. The network delay entails undesirable effects on the stability of the UAV control system due to delayed state feedback and outdated control input. Although several networked control algorithms have been proposed to deal with the network delay, most existing studies have assumed that the plant dynamics is known and simple, or the network delay is constant. These assumptions are improper to multirotor-type UAVs because of their nonlinearity and time-sensitive characteristics. To deal with these problems, we propose a networked control system using model predictive control (MPC) designed under the consideration of multirotor characteristics. We also apply a Gaussian process (GP) to learn an unknown nonlinear model, which increases the accuracy of path planning and state estimation. Flight experiments show that the proposed algorithm successfully compensates the network delay and Gaussian process learning improves the UAV's path tracking performance.N

Efficient networked UAV control using event-triggered predictive control

In this paper, we propose a method to improve the networked UAV control system using event-triggered control and model predictive control (MPC). Although the UAV control over the network has many advantages, it involves a long-time delay and packet loss, which adversely affect real-time control performance. Delay compensation algorithms in the networked control system (NCS) have been proposed to address such issues, however, they do not consider the resource limit of the network so that the network congestion may occur. In that case, the packet loss and network delay issues can even be worsened. In this study, we propose a method to reduce the generation of less important control signals and to use the network more efficiently by using event-triggered control. Since the event-triggered control method is also influenced by the network delay, an event trigger function suitable for NCS is designed. We validated the effectiveness of networked UAV control system and event-triggered control by simulation. (C) 2019, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.N

Robust Trajectory Planning for a Multirotor against Disturbance based on Hamilton-Jacobi Reachability Analysis

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.N

Real-time Optimal Planning and Model Predictive Control of a Multi-rotor with a Suspended Load

This paper presents planning and control algorithms for a multi-rotor with a suspended load. The suspended load cannot be controlled easily by the multi-rotor due to severe dynamic coupling between them. Difficulties are exacerbated by under-actuated, highly nonlinear nature of multi-rotor dynamics. Although many studies have been proposed to plan trajectories and control this system, there exist only a few reports on real-time trajectory generation. With this in mind, we propose a planning method which is capable of generating collision-free trajectories real-time and applicable to a high-dimensional nonlinear system. Using a differential flatness property, the system can be linearized entirely with elaborately chosen flat outputs. Convexification of non-convex constraints is carried out, and concave obstacle-avoidance constraints are converted to convex ones. After that, a convex optimization problem is solved to generate an optimal trajectory, but semi-feasible trajectory which considers only some parts of the initial state. We apply model predictive control with a sequential linear quadratic solver to compute a feasible collision-free trajectory and to control the system. Performance of the algorithm is validated by flight experiment.N