RGB-D saliency detection aims to fuse multi-modal cues to accurately localize salient regions. Existing works often adopt attention modules for feature modeling, with few methods explicitly leveraging fine-grained details to merge with semantic cues. Thus, despite the auxiliary depth information, it is still challenging for existing models to distinguish objects with similar appearances but at distinct camera distances. In this paper, from a new perspective, we propose a novel Hierarchical Depth Awareness network (HiDAnet) for RGB-D saliency detection. Our motivation comes from the observation that the multi-granularity properties of geometric priors correlate well with the neural network hierarchies. To realize multi-modal and multi-level fusion, we first use a granularity-based attention scheme to strengthen the discriminatory power of RGB and depth features separately. Then we introduce a unified cross dual-attention module for multi-modal and multi-level fusion in a coarse-to-fine manner. The encoded multi-modal features are gradually aggregated into a shared decoder. Further, we exploit a multi-scale loss to take full advantage of the hierarchical information. Extensive experiments on challenging benchmark datasets demonstrate that our HiDAnet performs favorably over the state-of-the-art methods by large margins. The source code can be found in https://github.com/Zongwei97/HIDANet/.

A homography-based dynamic control approach applied to station keeping of autonomous underwater vehicles (AUVs) without relying on linear velocity measurements is proposed. The homography estimated from images of a planar target scene captured by a downward-looking camera is directly used as feedback information. The full dynamics of the AUV are exploited in a hierarchical control design with inner and outer loop architectures. Enhanced by integral compensation actions and disturbance torque estimation, the proposed controller is robust with respect to model uncertainties and unknown currents. The performance of the proposed control approach is illustrated using both comparative simulation results conducted on a realistic AUV model and experimental validations on an in-house AUV.

Visual servoing control schemes, such as Image-Based (IBVS), Pose Based (PBVS) or Hybrid-Based (HBVS) have been extensively developed over the last decades making possible their uses in a large number of applications. It is well-known that the main problems to be handled concern the presence of local minima or singularities, the visibility constraint, the joint limits, etc. Recently, Model Predictive Path Integral (MPPI) control algorithm has been developed for autonomous robot navigation tasks. In this paper, we propose a MPPI-VS framework applied for the control of a 6-DoF robot with 2D point, 3D point, and Pose Based Visual Servoing techniques. We performed intensive simulations under various operating conditions to show the potential advantages of the proposed control framework compared to the classical schemes. The effectiveness, the robustness and the capability in coping easily with the system constraints of the control framework are shown.

Spherical cameras and the latest image processing techniques open up new horizons. In particular, methods based on Convolutional Neural Networks (CNNs) now give excellent results for optical flow estimation on perspective images. However, these approaches are highly dependent on their architectures and training datasets. This paper proposes to benefit from years of improvement in perspective images optical flow estimation and to apply it to omnidirectional ones without training on new datasets. Our network, OmniFlowNet, is built on a CNN specialized in perspective images. Its convolution operation is adapted to be consistent with the equirectangular projection. Tested on spherical datasets created with Blender ¹ 1https://www.blender.org and several equirectangular videos realized from real indoor and outdoor scenes, OmniFlowNet shows better performance than its original network without extra training.

We consider the problem in which a supervisor or remote pilot provides a real-time linear velocity reference to a multirotor aerial robot, either through a traditional remote control handset, a modern haptic interface, or semi-autonomous guidance control system. In all such cases, the goal is to servo-control the vehicle's velocity to the set point as quickly and as efficiently as possible. The challenge is to achieve this robustly in the presence of unknown wind disturbances and in situations in which the vehicle moves into global position system (GPS) denied environments (indoors, urban canyons, forests) where estimation of the vehicle's velocity is challenging. These situations include unclutterred environments, poor visibility environments caused by poor lighting, and poorly textured visual environments where laser- and vision-based sensors become unreliable. The approach taken is to develop a coupled nonlinear complementary velocity aided attitude filter that provides estimates of both the inertial and body-fixed frame linear velocities, as well as the attitude of a multirotor aerial vehicle, that functions effectively even when only the inertial measurement unit and barometric sensor measurements are available. When full inertial velocity measurements are available (from GPS, Vicon, or a vision system), the filter additionally estimates the external wind speed. In this paper, we formally present the proposed filter along with experimental results and a comparison of the filter to recent results in the literature and in situations in which inertial reference frame velocities are available intermittently. The proposed filter is computationally simple to implement, easy to calibrate and tune, and provides an excellent base level functionality for modern multirotor aerial robotic systems that will be required to function robustly in a variety of environments.