skip to main content


Title: Compressive Sensing-Based Reconstruction of Lissajous-Like Nodding Lidar Data
In this article, a compressive sensing (CS) reconstruction algorithm is applied to data acquired from a nodding multi-beam Lidar system following a Lissajous-like trajectory. Multi-beam Lidar systems provide 3D depth information of the environment for applications in robotics, but the vertical resolution of these devices may be insufficient to identify objects, especially when the object is small and/or far from the robot. In order to overcome this issue, the Lidar can be nodded in order to obtain higher vertical resolution with the side-effect of increased scan time, especially when raster scan patterns are used. Such systems, especially when combined with nodding, also yield large volumes of data which may be difficult to store and mange on resource constrained systems. Using Lissajous-like nodding trajectories allows for the trade-off between scan time and horizontal and vertical resolutions through the choice of scan parameters. These patterns also naturally sub-sample the imaged area and the data can be further reduced by simply not collecting each data point along the trajectory. The final depth image must then be reconstructed from the sub-sampled data. In this article, a CS reconstruction algorithm is applied to data collected during a fast and therefore low-resolution Lissajous-like scan. Experiments and simulations show the feasibility of this method and compare its results to images produced from simple nearest-neighbor interpolation.  more » « less
Award ID(s):
1658696
NSF-PAR ID:
10159200
Author(s) / Creator(s):
; ; ;
Date Published:
Journal Name:
2019 ASME Dynamic Systems and Control Conference
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
More Like this
  1. Abstract In this article, a compressive sensing-based reconstruction algorithm is applied to data acquired from a nodding multibeam Lidar system following a Lissajous-like trajectory. Multibeam Lidar systems provide 3D depth information of the environment, but the vertical resolution of these devices may be insufficient in many applications. To mitigate this issue, the Lidar can be nodded to obtain higher vertical resolution at the cost of increased scan time. Using Lissajous-like nodding trajectories allows for the trade-off between scan time and horizontal and vertical resolutions through the choice of scan parameters. These patterns also naturally subsample the imaged area. In this article, a compressive sensing-based reconstruction algorithm is applied to the data collected during a relatively fast and therefore low-resolution Lissajous-like scan. Experiments and simulations show the feasibility of this method and compare the reconstructions to those made using simple nearest-neighbor interpolation. 
    more » « less
  2. Abstract

    In situ digital inline holography is a technique which can be used to acquire high‐resolution imagery of plankton and examine their spatial and temporal distributions within the water column in a nonintrusive manner. However, for effective expert identification of an organism from digital holographic imagery, it is necessary to apply a computationally expensive numerical reconstruction algorithm. This lengthy process inhibits real‐time monitoring of plankton distributions. Deep learning methods, such as convolutional neural networks, applied to interference patterns of different organisms from minimally processed holograms can eliminate the need for reconstruction and accomplish real‐time computation. In this article, we integrate deep learning methods with digital inline holography to create a rapid and accurate plankton classification network for 10 classes of organisms that are commonly seen in our data sets. We describe the procedure from preprocessing to classification. Our network achieves 93.8% accuracy when applied to a manually classified testing data set. Upon further application of a probability filter to eliminate false classification, the average precision and recall are 96.8% and 95.0%, respectively. Furthermore, the network was applied to 7500 in situ holograms collected at East Sound in Washington during a vertical profile to characterize depth distribution of the local diatoms. The results are in agreement with simultaneously recorded independent chlorophyll concentration depth profiles. This lightweight network exemplifies its capability for real‐time, high‐accuracy plankton classification and it has the potential to be deployed on imaging instruments for long‐term in situ plankton monitoring.

     
    more » « less
  3. In this work, point pattern estimators are used to analyze the distribution of measurements from a multi-beam Lidar on a pitching platform. Multi-beam Lidars have high resolution in the horizontal plane, but poor vertical resolution. Placing the Lidar on a pitching base improves this resolution, but causes the distribution of measurements to be highly irregular. In this work, these measurement distributions are treated as point patterns and three estimators are used to quantity how measurements are spaced, which has implications in robotic detection of objects using Lidar sensors. These estimators are used to demonstrate how a pitching trajectory for the platform can be chosen to improve multiple performance criteria, such as increasing the likelihood of detection of an object, or adjusting how closely measurements should be spaced. 
    more » « less
  4. Abstract

    Erosive beach scarps influence beach vulnerability, yet their formation remains challenging to predict. In this study, a 1:2.5 scale laboratory experiment was used to study the subsurface hydrodynamics of a beach dune during an erosive event. Pressure and moisture sensors buried within the dune were used both to monitor the water table and to examine vertical pressure gradients in the upper 0.3 m of sand as the slope of the upper beach developed into a scarp. Concurrently, a line‐scan lidar tracked swash bores and monitored erosion and accretion patterns along a single cross‐shore transect throughout the experiment. As wave conditions intensified, a discontinuity in the slope of the dune formed; the discontinuity grew steeper and progressed landward at the same rate as theR2%runup extent until it was a fully formed scarp with a vertical face. Within the upper 0.15 m of the partially saturated sand, upward pore pressure gradients were detected during backwash, influencing the effective weight of sand and potentially contributing to beachface erosion. The magnitude and frequency of the upward pressure gradients increased with deeper swash depths and with frequency of wave interaction, and decreased with depth into the sand. A simple conceptual model for scarp formation is proposed that incorporates observations of upward‐directed pressure gradients from this study while providing a reference for future studies seeking to integrate additional swash zone sediment transport processes that may impact scarp development.

     
    more » « less
  5. Abstract

    Trajectory planning for multiple robots in shared environments is a challenging problem especially when there is limited communication available or no central entity. In this article, we present Real-time planning using Linear Spatial Separations, or RLSS: a real-time decentralized trajectory planning algorithm for cooperative multi-robot teams in static environments. The algorithm requires relatively few robot capabilities, namely sensing the positions of robots and obstacles without higher-order derivatives and the ability of distinguishing robots from obstacles. There is no communication requirement and the robots’ dynamic limits are taken into account. RLSS generates and solves convex quadratic optimization problems that are kinematically feasible and guarantees collision avoidance if the resulting problems are feasible. We demonstrate the algorithm’s performance in real-time in simulations and on physical robots. We compare RLSS to two state-of-the-art planners and show empirically that RLSS does avoid deadlocks and collisions in forest-like and maze-like environments, significantly improving prior work, which result in collisions and deadlocks in such environments.

     
    more » « less