About the potential of lidars with different photodetectors under daytime sky radiation

Results of theoretical analysis and experimental developments implemented as advanced methods and means to improve a noise-immunity of lidar systems for practical applications are discussed. A particular attention is paid to assessing the developed methods and technical solutions effectiveness and their comparison with existing lidar systems and real receivers. © 2016 Owned by the authors, published by EDP Sciences.Peer ReviewedPostprint (published version

Assessment of capabilities of lidar systems in day-and night-time under different atmospheric and internal-noise conditions

As an application of the dimensionless parameterization concept proposed earlier for the characterization of lidar systems, the universal assessment of lidar capabilities in day and night conditions is considered. The dimensionless parameters encapsulate the atmospheric conditions, the lidar optical and optoelectronic characteristics, including the photodetector internal noise, and the sky background radiation. Approaches to ensure immunity of the lidar system to external background radiation are discussed. © The Authors, published by EDP Sciences, 2018.Peer ReviewedPostprint (published version

Self-Supervised Depth Correction of Lidar Measurements from Map Consistency Loss

Depth perception is considered an invaluable source of information in the context of 3D mapping and various robotics applications. However, point cloud maps acquired using consumer-level light detection and ranging sensors (lidars) still suffer from bias related to local surface properties such as measuring beam-to-surface incidence angle, distance, texture, reflectance, or illumination conditions. This fact has recently motivated researchers to exploit traditional filters, as well as the deep learning paradigm, in order to suppress the aforementioned depth sensors error while preserving geometric and map consistency details. Despite the effort, depth correction of lidar measurements is still an open challenge mainly due to the lack of clean 3D data that could be used as ground truth. In this paper, we introduce two novel point cloud map consistency losses, which facilitate self-supervised learning on real data of lidar depth correction models. Specifically, the models exploit multiple point cloud measurements of the same scene from different view-points in order to learn to reduce the bias based on the constructed map consistency signal. Complementary to the removal of the bias from the measurements, we demonstrate that the depth correction models help to reduce localization drift. Additionally, we release a data set that contains point cloud data captured in an indoor corridor environment with precise localization and ground truth mapping information.Comment: Accepted to RA-L 2023: https://www.ieee-ras.org/publications/ra-

Spatial filtering efficiency of monostatic biaxial lidar: analysis and applications

Results of lidar modeling based on spatial-angular filtering efficiency criteria are presented. Their analysis shows that the low spatial-angular filtering efficiency of traditional visible and near-infrared systems is an important cause of low signal background-radiation ratio SBR at the photodetector input. The low SBR may be responsible for considerable measurement errors and ensuing the low accuracy of the retrieval of atmospheric optical parameters. As shown, the most effective protection against sky background radiation for groundbased biaxial lidars is the modifying of their angular field according to a spatial-angular filtering efficiency criterion. Some effective approaches to achieve a high filtering efficiency for the receiving system optimization are discussed.Postprint (published version

MonoForce: Self-supervised learning of physics-aware grey-box model for predicting the robot-terrain interaction

We introduce an explainable, physics-aware, and end-to-end differentiable model which predicts the outcome of robot-terrain interaction from camera images. The proposed MonoForce model consists of a black-box module, which predicts robot-terrain interaction forces from the onboard camera, followed by a white-box module, which transforms these forces through the laws of classical mechanics into the predicted trajectories. As the white-box model is implemented as a differentiable ODE solver, it enables measuring the physical consistency between predicted forces and ground-truth trajectories of the robot. Consequently, it creates a self-supervised loss similar to MonoDepth. To facilitate the reproducibility of the paper, we provide the source code. See the project github for codes and supplementary materials such as videos and data sequences

SwarmCloak: Landing of a Swarm of Nano-Quadrotors on Human Arms

We propose a novel system SwarmCloak for landing of a fleet of four flying robots on the human arms using light-sensitive landing pads with vibrotactile feedback. We developed two types of wearable tactile displays with vibromotors which are activated by the light emitted from the LED array at the bottom of quadcopters. In a user study, participants were asked to adjust the position of the arms to land up to two drones, having only visual feedback, only tactile feedback or visual-tactile feedback. The experiment revealed that when the number of drones increases, tactile feedback plays a more important role in accurate landing and operator's convenience. An important finding is that the best landing performance is achieved with the combination of tactile and visual feedback. The proposed technology could have a strong impact on the human-swarm interaction, providing a new level of intuitiveness and engagement into the swarm deployment just right from the skin surface.Comment: ACM Siggraph Asia 2019 conference (Emerging Technologies section). Best Demo Award by committee member

Updating the petrophysical model of a thin-layered clay reservoir, taking into account the identified lithofacies features

The article presents a study on the establishment of geological and facies conditions for the formation of a sandy-silty-clayey reservoir based on core analysis and linking the information obtained with neighboring areas. Previously used petrophysical relationships were generalized with underlying objects composed of thick monolithic sandstones and did not allow correctly assessing the properties of the reservoir under consideration. The work made it possible to clarify the idea of lithofacies features to substantiate their own petrophysical dependencies, which make it possible to differentiate thin-layered clay reservoirs in the section and determine their properties. The results obtained during the study made it possible to refine the petrophysical model, previously generalized to all the layers of the group. The revision of the boundary values and dependencies helped to justify the increase in reservoir oil reserves and became the basis for subsequent work to increase the profitability of putting the facility into commercial development