In this paper, we propose a novel active illumination framework to enhance the performance of VO and V-SLAM algorithms in these challenging conditions. The developed approach dynamically controls a moving light source to illuminate highly textured areas, thereby improving feature extraction and tracking. Specifically, a detector block, which incorporates a deep learning-based enhancing network, identifies regions with relevant features. Then, a pan-tilt controller is responsible for guiding the light beam toward these areas, so that to provide information-rich images to the ego-motion estimation algorithm. Experimental results on a real robotic platform demonstrate the effectiveness of the proposed method, showing a reduction in the pose estimation error up to 75% with respect to a traditional fixed lighting technique. I. INTRODUCTION Vision-based pose estimation is one of the most widespread strategies to achieve mobile robot localization. Several effective Visual Odometry (VO) and Visual SLAM (V -SLAM) approaches have flourished in the last decades [1], and the recent emergence of visual-inertial techniques has shown even more impressive results [2], [3]. The effectiveness of VO and V -SLAM solutions depends on the capability to extract robust and highly-descriptive visual features.

Over the past decade, there has been extensive work in developing integrated silicon photonics (SiPh) gratings for the optical addressing of trapped ion qubits in the ion trap quantum computing community. However, when viewing beam profiles from infrared (IR) cameras, it is often difficult to determine the corresponding heights where the beam profiles are located. In this work, we developed transformer models to recognize the corresponding height categories of beam profiles of light from SiPh gratings. The model is trained using two techniques: (1) input patches, and (2) input sequence. For model trained with input patches, the model achieved recognition accuracy of 0.938. Meanwhile, model trained with input sequence shows lower accuracy of 0.895. However, when repeating the model-training 150 cycles, model trained with input patches shows inconsistent accuracy ranges between 0.445 to 0.959, while model trained with input sequence exhibit higher accuracy values between 0.789 to 0.936. The obtained outcomes can be expanded to various applications, including auto-focusing of light beam and auto-adjustment of z-axis stage to acquire desired beam profiles.

Talking head generation based on the neural radiation fields model has shown promising visual effects. However, the slow rendering speed of NeRF seriously limits its application, due to the burdensome calculation process over hundreds of sampled points to synthesize one pixel. In this work, a novel Neural Light Dynamic Fields model is proposed aiming to achieve generating high quality 3D talking face with significant speedup. The NLDF represents light fields based on light segments, and a deep network is used to learn the entire light beam's information at once. In learning the knowledge distillation is applied and the NeRF based synthesized result is used to guide the correct coloration of light segments in NLDF. Furthermore, a novel active pool training strategy is proposed to focus on high frequency movements, particularly on the speaker mouth and eyebrows. The propose method effectively represents the facial light dynamics in 3D talking video generation, and it achieves approximately 30 times faster speed compared to state of the art NeRF based method, with comparable generation visual quality.

Designing and validating sensor applications and algorithms in simulation is an important step in the modern development process. Furthermore, modern open-source multi-sensor simulation frameworks are moving towards the usage of video-game engines such as the Unreal Engine. Simulation of a sensor such as a LiDAR can prove to be difficult in such real-time software. In this paper we present a GPU-accelerated simulation of LiDAR based on its physical properties and interaction with the environment. We provide a generation of the depth and intensity data based on the properties of the sensor as well as the surface material and incidence angle at which the light beams hit the surface. It is validated against a real LiDAR sensor and shown to be accurate and precise although highly depended on the spectral data used for the material properties.

There's no easy fix to America's supply chain problem, but a major issue is a lack of drivers. Now a new type of scanner called 4-D LiDar offers a possible solution, which could be a huge step forward for self-driving cars and trucks. Anyone who has ever been temporarily blinded by high-beam headlights from an oncoming car will be happy to hear this. Audi's lighting tech can be focused enough to project images on a wall. U.S. highway safety regulators are about to allow new high-tech headlights that can automatically tailor beams so they focus on dark areas of the road and don't create glare for oncoming drivers.

Plants are dynamic systems that are integral to our existence and survival. Plants face environment changes and adapt over time to their surrounding conditions. We argue that plant responses to an environmental stimulus are a good example of a real-world problem that can be approached within a reinforcement learning (RL)framework. With the objective of controlling a plant by moving the light source, we propose GrowSpace, as a new RL benchmark. The back-end of the simulator is implemented using the Space Colonisation Algorithm, a plant growing model based on competition for space. Compared to video game RL environments, this simulator addresses a real-world problem and serves as a test bed to visualize plant growth and movement in a faster way than physical experiments. GrowSpace is composed of a suite of challenges that tackle several problems such as control, multi-stage learning,fairness and multi-objective learning. We provide agent baselines alongside case studies to demonstrate the difficulty of the proposed benchmark.

In work that could someday turn cell phones into sensors capable of detecting viruses and other minuscule objects, MIT researchers have built a powerful nanoscale flashlight on a chip. Their approach to designing the tiny light beam on a chip could also be used to create a variety of other nano flashlights with different beam characteristics for different applications. Think of a wide spotlight versus a beam of light focused on a single point. For many decades, scientists have used light to identify a material by observing how that light interacts with the material. They do so by essentially shining a beam of light on the material, then analyzing that light after it passes through the material.

Invisibility is no longer science fiction. Researchers have developed a unique light wave that, when beamed through an object, makes the object appear invisible to cameras and even the human eye. The backstory: If you think invisibility cloaks are only for wizards, think again. Scientists have been trying to solve this challenge since long before Dumbledore bestowed the hallow cloak upon Harry Potter, and invisibility tech is for real. With the tricks of the camera, scientists can capture pictures of what's behind an object, then project them onto the object's surface, making it seem to disappear.

Machine learning has been used by scientists in the US to reduce unwanted fluctuations in photon beams from a synchrotron light source. The technique does this by stabilizing the synchrotron's electron beam and offers a way around an important barrier to the development of next-generation facilities. The work was done by Simon Leemann and colleagues at the Lawrence Berkeley National Laboratory (LBNL) in California and could allow emerging analysis techniques that require high beam stability – such as X-ray photon correlation spectroscopy (XPCS) – to be implemented on synchrotons. Synchrotron light sources are extremely useful scientific instruments because they deliver bright, high-quality beams of coherent electromagnetic radiation from infrared wavelengths up to soft X-rays. The light is produced by accelerating electrons in a storage ring using powerful magnets – taking advantage of the fact that an accelerated electron emits electromagnetic radiation.

Synchrotron light sources are powerful facilities that produce light in a variety of "colors," or wavelengths--from the infrared to X-rays--by accelerating electrons to emit light in controlled beams. Synchrotrons like the Advanced Light Source at the Department of Energy's Lawrence Berkeley National Laboratory (Berkeley Lab) allow scientists to explore samples in a variety of ways using this light, in fields ranging from materials science, biology, and chemistry to physics and environmental science. Researchers have found ways to upgrade these machines to produce more intense, focused, and consistent light beams that enable new, and more complex and detailed studies across a broad range of sample types. Many of these synchrotron facilities deliver different types of light for dozens of simultaneous experiments. And little tweaks to enhance light-beam properties at these individual beamlines can feed back into the overall light-beam performance across the entire facility.