In this paper, visual servoing based on photometric moments is advocated. A direct approach is chosen by which the extraction of geometric primitives, visual tracking and image matching steps of a conventional visual servoing pipeline can be bypassed. A vital challenge in photometric methods is the change in the image resulting from the appearance and disappearance of portions of the scene from the camera field of view during the servo. To tackle this issue, a general model for the photometric moments enhanced with spatial weighting is proposed. The interaction matrix for these spatially weighted photometric moments is derived in an analytical form. The correctness of the modeling, effectiveness of the proposed strategy in handling the exogenous regions, and improved convergence domain are demonstrated with a combination of simulation and the experimental results.

Humans tend to build environments with structure, which consists of mainly planar surfaces. From the intersection of planar surfaces arise straight lines. Lines have more degrees of freedom than points. Thus, line-based structure-from-motion (SfM) provides more information about the environment. In this article, we present solutions for SfM using lines, namely, incremental SfM. These approaches consist of designing state observers for a camera’s dynamical visual system looking at a 3-D line. We start by presenting a model that uses spherical coordinates for representing the line’s moment vector. We show that this parameterization has singularities, and, therefore, we introduce a more suitable model that considers the line’s moment and shortest viewing ray. Concerning the observers, we present two different methodologies. The first uses a memory-less state-of-the-art framework for dynamic visual systems. Since the previous states of the robotic agent are accessible—while performing the 3-D mapping of the environment—the second approach aims at exploiting the use of memory to improve the estimation accuracy and convergence speed. The two models and the two observers are evaluated in simulation and real data, where mobile and manipulator robots are used.

Conventional active incremental structure from motion (ISfM) schemes require a precise knowledge of the linear and angular velocities of the vision system to compute the three-dimensional structure of the observed scene. Furthermore, they are generally coupled and nonlinear, which makes the reconstruction inaccurate. In this paper, we present a novel active ISfM scheme to overcome these difficulties. It relies on two appropriate transformations: the first one allows efficient decoupling of the nonlinear model, whereas the second one transforms the decoupled model into the Brunovsky's linear form. We then show that a Luenberger's observer can be designed to estimate the unavailable states, making our scheme globally asymptotically convergent and robust to noise on measurements. We present simulated and experimental results to illustrate the performance of the described methodology.

In this paper, we present EC-WAMI, the first successful application of neuromorphic event cameras (ECs) for Wide-Area Motion Imagery (WAMI) and Remote Sensing (RS), showcasing their potential for advancing Structure-from-Motion (SfM) and 3D reconstruction across diverse imaging scenarios. ECs, which detect asynchronous pixel-level brightness changes, offer key advantages over traditional frame-based sensors such as high temporal resolution, low power consumption, and resilience to dynamic lighting. These capabilities allow ECs to overcome challenges such as glare, uneven lighting, and low-light conditions that are common in aerial imaging and remote sensing, while also extending UAV flight endurance. To evaluate the effectiveness of ECs in WAMI, we simulate event data from RGB WAMI imagery and integrate them into SfM pipelines for camera pose optimization and 3D point cloud generation. Using two state-of-the-art SfM methods, namely, COLMAP and Bundle Adjustment for Sequential Imagery (BA4S), we show that although ECs do not capture scene content like traditional cameras, their spike-based events, which only measure illumination changes, allow for accurate camera pose recovery in WAMI scenarios even in low-framerate(5 fps) simulations. Our results indicate that while BA4S and COLMAP provide comparable accuracy, BA4S significantly outperforms COLMAP in terms of speed. Moreover, we evaluate different feature extraction methods, showing that the deep learning-based LIGHTGLUE descriptor consistently outperforms traditional handcrafted descriptors by providing improved reliability and accuracy of event-based SfM. These results highlight the broader potential of ECs in remote sensing, aerial imaging, and 3D reconstruction beyond conventional WAMI applications. Our dataset will be made available for public use.

Accurate object pose estimation is vital in robotics and satellite operations, especially during rendezvous missions involving proximity interactions with orbiting satellites, non-cooperative objects, or space debris. Traditional frame-based cameras struggle in the space environment due to limitations such as motion blur, low dynamic range, and low frame rates—conditions exacerbated by rapid relative motion, abrupt lighting changes, and high contrast variations in orbit. Event cameras offer a compelling alternative with their unique advantages: ultra-high temporal resolution (>10,000 fps), wide dynamic range (up to 140 dB), and low power consumption (1mW), making them especially suited for the extreme visual conditions in space. This paper introduces EvSat3D, a comprehensive and robust pipeline for event-based satellite pose estimation and 3D reconstruction. The pipeline leverages event-to-frame (E2F) conversion algorithms to generate frames from asynchronous event data and applies Structure-from-Motion (SfM) techniques for accurate camera pose recovery and 3D scene reconstruction. We validate EvSat3D using the only publicly available event-based satellite dataset and demonstrate its effectiveness under challenging conditions. Our results highlight the feasibility and performance benefits of using event cameras for satellite navigation and pose estimation tasks in space.