Information-Driven Adaptive Structured-Light Scanners

Rosman, Guy; Rus, Daniela; Fisher, John W.

doi:10.1109/cvpr.2016.101

Cited by 10 publications

(3 citation statements)

References 31 publications

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“…Although, there has been work in the robotics and vision community on adaptive sensing of features in the scene relevant to a particular inference task, they do not incorporate the working principles of a particular 3D sensor in their algorithm design (Denzler and Brown, 2002;Paletta et al, 2000). In the field of structured light, Zhang et al (2014) and Rosman et al (2016) incorporate the number of projected patterns as a resource expenditure which they try to minimize while maximizing the information gain from the scene. In this article, we are interested in adaptive algorithms for LiDAR sensors that take into account physical constraints such as the power expended on far away objects or on objects moving out of the FOV.…”

Section: Adaptive Sampling In 3d Modelsmentioning

confidence: 99%

Adaptive fovea for scanning depth sensors

Tasneem

Adhivarahan

Wang

et al. 2020

The International Journal of Robotics Research

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Depth sensors have been used extensively for perception in robotics. Typically these sensors have a fixed angular resolution and field of view (FOV). This is in contrast to human perception, which involves foveating: scanning with the eyes’ highest angular resolution over regions of interest (ROIs). We build a scanning depth sensor that can control its angular resolution over the FOV. This opens up new directions for robotics research, because many algorithms in localization, mapping, exploration, and manipulation make implicit assumptions about the fixed resolution of a depth sensor, impacting latency, energy efficiency, and accuracy. Our algorithms increase resolution in ROIs either through deconvolutions or intelligent sample distribution across the FOV. The areas of high resolution in the sensor FOV act as artificial fovea and we adaptively vary the fovea locations to maximize a well-known information theoretic measure. We demonstrate novel applications such as adaptive time-of-flight (TOF) sensing, LiDAR zoom, gradient-based LiDAR sensing, and energy-efficient LiDAR scanning. As a proof of concept, we mount the sensor on a ground robot platform, showing how to reduce robot motion to obtain a desired scanning resolution. We also present a ROS wrapper for active simulation for our novel sensor in Gazebo. Finally, we provide extensive empirical analysis of all our algorithms, demonstrating trade-offs between time, resolution and stand-off distance.

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Section: Adaptive Sampling In 3d Modelsmentioning

confidence: 99%

Adaptive fovea for scanning depth sensors

Tasneem

Adhivarahan

Wang

et al. 2020

The International Journal of Robotics Research

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“…There is an extensive literature on reasoning about sensor placement. In computer vision this is often addressed as part of active perception [3], [5], [34]. In robotics, such efforts are part of next-best-view planning and dynamic planning [1], [15], [41], [24], [33], [19], although in our case we estimate planner-level notions (task success) rather than lower-level ones (geometric or classification uncertainty).…”

Section: Related Workmentioning

confidence: 99%

Task-Specific Sensor Planning for Robotic Assembly Tasks

Rosman¹,

Choi²,

Dogar

et al. 2018

2018 IEEE International Conference on Robotics and Automation (ICRA)

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When performing multi-robot tasks, sensory feedback is crucial in reducing uncertainty for correct execution. Yet the utilization of sensors should be planned as an integral part of the task planning, taken into account several factors such as the tolerance of different inferred properties of the scene and interaction with different agents. In this paper we handle this complex problem in a principled, yet efficient way. We use surrogate predictors based on openloop simulation to estimate and bound the probability of success for specific tasks. We reason about such task-specific uncertainty approximants and their effectiveness. We show how they can be incorporated into a multi-robot planner, and demonstrate results with a team of robots performing assembly tasks.

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“…Structured light illumination (SLI) refers to a method of 3D scanning that uses a projector to project a series of light striped patterns such that a camera can reconstruct depth based on the warping of the pattern over the target object's surface [22,10,16,14,19,13,28,27,31]. Examples of SLI include single pattern techniques which project a static pattern that is continuously projected and from which a 3D reconstruction can be made from a single snap shot [10,2,11,8].…”

Section: Introductionmentioning

confidence: 99%

Causes and Corrections for Bimodal Multi-Path Scanning With Structured Light

Lau

2019

2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

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Structured light illumination is an active 3-D scanning technique based on projecting/capturing a set of striped patterns and measuring the warping of the patterns as they reflect off a target object's surface. As designed, each pixel in the camera sees exactly one pixel from the projector; however, there are exceptions to this when the scanned surface has a complicated geometry with step edges and other discontinuities in depth or where the target surface has specularities that reflect light away from the camera. These situations are generally referred to multipath where a given camera pixel receives light from multiple positions from the projector. In the case of bimodal multipath, the camera pixel receives light from exactly two positions from the projector which occurs when light bounce back from a reflective surface or along a step edge where the edge slices through a pixel so that the pixel sees both a foreground and background surface. In this paper, we present a general mathematical model and address the bimodal multipath issue in a phase measuring profilometry scanner to measure the constructive and destructive interference between the two light paths, and by taking advantage of this interesting cue, separate the paths and make two separated depth measurements. We also validate our algorithm with both simulation and a number of challenging real cases.

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Information-Driven Adaptive Structured-Light Scanners

Cited by 10 publications

References 31 publications

Adaptive fovea for scanning depth sensors

Adaptive fovea for scanning depth sensors

Task-Specific Sensor Planning for Robotic Assembly Tasks

Causes and Corrections for Bimodal Multi-Path Scanning With Structured Light

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