Radar has become an essential sensor for autonomous navigation, especially in challenging environments where camera and LiDAR sensors fail. 4D single-chip millimeter-wave radar systems, in particular, have drawn increasing attention thanks to their ability to provide spatial and Doppler information with low hardware cost and power consumption. However, most single-chip radar systems using traditional signal processing, such as Fast Fourier Transform, suffer from limited spatial resolution in radar detection, significantly limiting the performance of radar-based odometry and Simultaneous Localization and Mapping (SLAM) systems. In this paper, we develop a novel radar signal processing pipeline that integrates spatial domain beamforming techniques, and extend it to 3D Direction of Arrival estimation. Experiments using public datasets are conducted to evaluate and compare the performance of our proposed signal processing pipeline against traditional methodologies. These tests specifically focus on assessing structural precision across diverse scenes and measuring odometry accuracy in different radar odometry systems. This research demonstrates the feasibility of achieving more accurate radar odometry by simply replacing the standard FFT-based processing with the proposed pipeline. The codes are available at GitHub**https://github.com/SenseRoboticsLab/DBE-Radar.

Multi-sensor fusion is a mainstream method for localization of unmanned systems. How to achieve 6-degrees of freedom (DOF) pose estimation of the system is challenging in GPS-denied environments. Although map-aided localization methods normally perform well on intelligent transportation systems, prior maps are unavailable in some GPS-denied scenes (e.g., dense forests, tunnels, and underground parking lots). In this paper, we present a robust and consistent monocular visual-inertial-depth odometry (VIDO) to perform 6-DOF pose estimation without the need of prior information. The system contains a visual-inertial subsystem (VIS) based on tightly coupled optimization in a sliding window and a depth subsystem (DS) based on the iterative closest point (ICP) estimation using 3D point clouds obtained by a LiDAR or depth camera. The uncertainties of the estimation results in VIS and DS are rigorously calculated to consider measurement noises of the sensors. The obtained uncertainty estimates are fed into a covariance intersection (CI) filter for pose fusion, and the fused pose is further refined in the mapping process. We perform experiments on public datasets, as well as in various real-world outdoor and indoor scenes to verify the performance on localization and mapping in urban areas with buildings and cars, off-road environments with rugged terrains, as well as indoor structured environments. The results show that the proposed method can provide both a robust 6-DOF pose estimate and a precise 3D map for fully autonomous navigation in different scenes without a prior map, which presents an attractive complement to map-aided automated driving.

This article proposes a novel adaptive image-based output feedback visual servoing approach to control a multirotor to the desired pose by using a minimum onboard sensor suite, which consists of an inertial measurement unit and a monocular camera. Different from “perspective moment,” a new type of image feature is designed as “rotated perspective moment,” whose dynamics is independent of roll, pitch, and yaw rates. On this basis, a nonlinear adaptive observer is designed to estimate the scaled linear velocity, which is more accurate, since the observer does not involve noisy angular velocity measurements. Then, a novel image-based output feedback controller is proposed with the designed image features and the observer, wherein the new saturated integral terms of linear and angular velocity errors are introduced into the controller design, respectively, to compensate system uncertainties. As a result, the steady-state error is decreased considerably. In addition, without the assumption of the separation principle between the observer and the controller, the small-angle approximation, or the time-scale separation assumption, the error signals of image features, attitude, velocity, and observer estimation can all converge to the origin asymptotically, which is proven by rigorous Lyapunov analysis. Comparative experiments are conducted to show the superior performance of the proposed approach in terms of more accurate velocity estimation, smaller steady-state errors, and stronger robustness.