In recent years, unsupervised deep learning approaches have received significant attention to estimating the depth and visual odometry (VO) from unlabelled monocular image sequences. However, their performance is limited in challenging environments due to perceptual degradation, occlusions, and rapid motions. Moreover, the existing unsupervised methods suffer from the lack of scale-consistency constraints across frames, which causes that the VO estimators fail to provide persistent trajectories over long sequences. In this study, we propose an unsupervised monocular deep VO framework that predicts a six-degrees-of-freedom pose camera motion and depth map of the scene from unlabelled RGB image sequences. We provide detailed quantitative and qualitative evaluations of the proposed framework on a) a challenging dataset collected during the DARPA Subterranean challenge1; and b) the benchmark KITTI and Cityscapes datasets. The proposed approach significantly outperforms state-of-the-art unsupervised deep VO and depth prediction methods under perceptually degraded conditions providing better results for both pose estimation and depth recovery. Furthermore, it achieves state-of-the-art results in most of the VO and depth metrics on benchmark datasets. The presented approach is part of the solution used by the COSTAR team participating in the DARPA Subterranean Challenge.

We present an approach for estimating the body-frame velocity of a mobile robot. We combine measurements from a millimeter-wave radar-on-a-chip sensor and an inertial measurement unit (IMU) in a batch optimization over a sliding window of recent measurements. The sensor suite employed is lightweight, low-power, and is invariant to ambient lighting conditions. This makes the proposed approach an attractive solution for platforms with limitations around payload and longevity, such as aerial vehicles conducting autonomous exploration in perceptually degraded operating conditions, including subterranean environments. We compare our radar-inertial velocity estimates to those from a visual-inertial (VI) approach. We show the accuracy of our method is comparable to VI in conditions favorable to VI, and far exceeds the accuracy of VI when conditions deteriorate.

Today's complex robotic designs comprise in some cases a large number of degrees of freedom, enabling for multi-objective task resolution (e.g., humanoid robots or aerial manipulators). This paper tackles the local stability problem of a hierarchical closed-loop inverse kinematics algorithm for such highly redundant robots. We present a method to guarantee this system stability by performing an online tuning of the closed-loop control gains. We define a semi-definite programming problem (SDP) with these gains as decision variables and a discrete-time Lyapunov stability condition as a linear matrix inequality, constraining the SDP optimization problem and guaranteeing the local stability of the prioritized tasks. To the best of authors' knowledge, this work represents the first mathematical development of an SDP formulation that introduces these stability conditions for a multi-objective closed-loop inverse kinematic problem for highly redundant robots. The validity of the proposed approach is demonstrated through simulation case studies, including didactic examples and a Matlab toolbox for the benefit of the community.

Recently, Differential Dynamic Programming (DDP) and other similar algorithms have become the solvers of choice when performing non-linear Model Predictive Control (nMPC) with modern robotic devices. The reason is that they have a lower computational cost per iteration when compared with off-the-shelf Non-Linear Programming (NLP) solvers, which enables its online operation. However, they cannot handle constraints, and are known to have poor convergence capabilities. In this paper, we propose a method to solve the optimal control problem with control bounds through a squashing function (i.e., a sigmoid, which is bounded by construction). It has been shown that a naive use of squashing functions damage the convergence rate. To tackle this, we first propose to add a quadratic barrier that avoids the difficulty of the plateau produced by the sigmoid. Second, we add an outer loop that adapts both the sigmoid and the barrier; it makes the optimal control problem with the squashing function converge to the original control-bounded problem. To validate our method, we present simulation results for different types of platforms including a multi-rotor, a biped, a quadruped and a humanoid robot.

This paper develops a simple and low-cost method for 3D, high-rate vehicle state estimation, specially designed for free-flying Micro Aerial Vehicles (MAVs). We fuse observations from inertial measurement units and the recently appeared low-cost optical flow smart cameras. These smart cameras integrate a sonar altimeter, a triaxial gyrometer and an optical flow sensor, and directly provide metric ego-motion information in the form of body velocities and altitude. Compared to state-of-the-art visual-inertial odometry methods, we are able to drastically reduce the computational load in the main processor unit, and obtain an accurate estimation of the vehicle state at a high update rate of 100Hz. We thus extend the current use of these smart cameras from hovering purposes to odometry estimation. In order to propose a simple algorithmic solution, we investigate the performances of two Kalman filters, in the extended and error-state flavors, alongside a large number of algorithm variations, using simulations and real experiments with precise ground-truth. We observe that the marginal performance gain attained with these algorithm improvements does not pay for the effort of implementing them. We conclude that a classical EKF in its simplest form is sufficient for providing motion estimates that coherently exploit the available measurements.

This paper develops a new method for uncalibrated image-based visual servoing. In contrast to traditional image-based visual servo, the proposed solution does not require a known value of camera focal length for the computation of the image Jacobian. Instead, it is estimated at run time from the observation of the tracked target. The technique is shown to outperform classical visual servoing schemes in situations with noisy calibration parameters and for unexpected changes in the camera zoom. The method's performance is demonstrated both in simulation experiments and in a ROS implementation of a quadrotor servoing task. The developed solution is tightly integrated with ROS and is made available as part of the IRI ROS stack.