The study of non-line-of-sight (NLOS) imaging is growing due to its many potential applications, including rescue operations and pedestrian detection by self-driving cars. However, implementing NLOS imaging on a moving camera remains an open area of research. Existing NLOS imaging methods rely on time-resolved detectors and laser configurations that require precise optical alignment, making it difficult to deploy them in dynamic environments. This work proposes a data-driven approach to NLOS imaging, PathFinder, that can be used with a standard RGB camera mounted on a small, power-constrained mobile robot, such as an aerial drone. Our experimental pipeline is designed to accurately estimate the 2D trajectory of a person who moves in a Manhattan-world environment while remaining hidden from the camera’s field-of-view. We introduce a novel approach to process a sequence of dynamic successive frames in a line-of-sight (LOS) video using an attention-based neural network that performs inference in real-time. The method also includes a preprocessing selection metric that analyzes images from a moving camera which contain multiple vertical planar surfaces, such as walls and building facades, and extracts planes that return maximum NLOS information. We validate the approach on in-the-wild scenes using a drone for video capture, thus demonstrating low-cost NLOS imaging in dynamic capture environments. The real-world dataset that we collected and used to train the network can be found at https://srchandr.github.io/DynamicNLOS/.

In recent years, small-scale driving testbeds have been developed as controlled physical environments for the evaluation of autonomous vehicle controllers. Such controllers are heavily dependent on computer vision algorithms that enable the vehicle to perceive its surroundings. To bridge the Sim2Real content and appearance gap between simulated and real-world image data for training these algorithms, we propose a novel transfer learning approach that performs domain adaptation using StyleGAN to generate style-mixed images that closely resemble real-world images. We explain our approach within the context of our small-scale driving testbed, CHARTOPOLIS, and demonstrate it on synthetic image data of two object classes, vehicles and buildings, from the driving simulator CARLA. Our results show that this approach works on the vehicle object class while failing on the building object class. This paper thus provides a plausible approach to bridging the Sim2Real gap through the use of custom pipelines that augment image datasets using a mix of techniques for domain adaptation and domain randomization.

Robust feature matching forms the backbone for most Visual Simultaneous Localization and Mapping (vSLAM), visual odometry, 3D reconstruction, and Structure from Motion (SfM) algorithms. However, recovering feature matches from texture-poor scenes is a major challenge and still remains an open area of research. In this paper, we present a Stereo Visual Odometry (StereoVO) technique based on point and line features which uses a novel feature-matching mechanism based on an Attention Graph Neural Network that is designed to perform well even under adverse weather conditions such as fog, haze, rain, and snow, and dynamic lighting conditions such as nighttime illumination and glare scenarios. We perform experiments on multiple real and synthetic datasets to validate our method's ability to perform StereoVO under low-visibility weather and lighting conditions through robust point and line matches. The results demonstrate that our method achieves more line feature matches than state-of-the-art line-matching algorithms, which when complemented with point feature matches perform consistently well in adverse weather and dynamic lighting conditions.

Recovering rigid registration between successive camera poses lies at the heart of 3D reconstruction, SLAM and visual odometry. Registration relies on the ability to compute discriminative 2D features in successive camera images for determining feature correspondences, which is very challenging in feature-poor environments, i.e. low-texture and/or low-light environments. In this paper, we aim to address the challenge of recovering rigid registration between successive camera poses in feature-poor environments in a Visual Inertial Odometry (VIO) setting. In addition to inertial sensing, we instrument a small aerial robot with an RGBD camera and propose a framework that unifies the incorporation of 3D geometric entities: points, lines, and planes. The tracked 3D geometric entities provide constraints in an Extended Kalman Filtering framework. We show that by directly exploiting 3D geometric entities, we can achieve improved registration. We demonstrate our approach on different texture-poor environments, with some containing only flat texture-less surfaces providing essentially no 2D features for tracking. In addition, we evaluate how the addition of different 3D geometric entities contributes to improved pose estimation by comparing an estimated pose trajectory to a ground truth pose trajectory obtained from a motion capture system. We consider computationally efficient methods for detecting 3D points, lines and planes, since our goal is to implement our approach on small mobile robots, such as drones.

The Go-CHART is a four-wheel, skid-steer robot that resembles a 1:28 scale standard commercial sedan. It is equipped with an onboard sensor suite and both onboard and external computers that replicate many of the sensing and computation capabilities of a full-size autonomous vehicle. The Go-CHART can autonomously navigate a small-scale traffic testbed, responding to its sensor input wiwithth programmed controllers. Alternatively, it can be remotely driven by a user who views the testbed through the robot's four camera feeds, which facilitates safe, controlled experiments on driver interactions with driverless vehicles. We demonstrate the Go-CHART's ability to perform lane tracking and detection of traffic signs, traffic signals, and other Go-CHARTs in real-time, utilizing an external GPU that runs computationally intensive computer vision and deep learning algorithms.