4D scan registration with the SR-3000 LIDAR

Stipes, J.A.; Cole, Justin G.; Humphreys, John F.

doi:10.1109/robot.2008.4543664

Cited by 6 publications

(7 citation statements)

References 12 publications

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“…Another adaptation of an ICP-like algorithm for ToF images is presented by Stipes et al [17], where both the depth and the intensity images are used. They present a probabilistic point sampling process to obtain significant points used in the registration process.…”

Section: Scene-related Tasksmentioning

confidence: 99%

“…one that can be approximated by a cylinder, and it [10] Obstacle avoidance in static env. 3D at high rate SR2 (depth) May et al [11,12] 3D mapping 3D at high rate/No required Pan-Tilt SR2 (depth) May et al [13] Pose estimation/3D mapping Registered depth-intensity SR3 (depth + intensity) Hedge and Ye [14] Planar feature 3D mapping 3D at high rate/No required Pan-Tilt SR3 Ohno et al [16] 3D mapping 3D at high rate SR2 Stipes et al [17] 3D mapping / Point selection Registered depth-intensity SR3 May et al [18] 3D mapping/SLAM 3D at high rate SR3 Gemeiner et al [19] Corner filtering Registered depth-intensity SR3 (depth + intensity) Thielemann et al [20] Navigation in pipelines 3D allow geometric primitives search SR3 Sheh et al [21] Navigation in hard env. 3D at high rate SR3 + inertial Swadzba et al [22] 3D mapping in dynamic env.…”

Section: Scene-related Tasksmentioning

confidence: 99%

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ToF cameras for active vision in robotics

Alenyà

Foix

Torras

2014

Sensors and Actuators A: Physical

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ToF cameras are now a mature technology that is widely being adopted to provide sensory input to robotic applications. Depending on the nature of the objects to be perceived and the viewing distance, we distinguish two groups of applications: those requiring to capture the whole scene and those centered on an object. It will be demonstrated that it is in this last group of applications, in which the robot has to locate and possibly manipulate an object, where the distinctive characteristics of ToF cameras can be better exploited.After presenting the physical sensor features and the calibration requirements of such cameras, we review some representative works highlighting for each one which of the distinctive ToF characteristics have been more essential. Even if at low resolution, the acquisition of 3D images at frame-rate is one of the most important features, as it enables quick background/foreground segmentation. A common use is in combination with classical color cameras. We present three developed applications, using a mobile robot and a robotic arm, to exemplify with real images some of the stated advantages.

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Section: Scene-related Tasksmentioning

confidence: 99%

Section: Scene-related Tasksmentioning

confidence: 99%

ToF cameras for active vision in robotics

Alenyà

Foix

Torras

2014

Sensors and Actuators A: Physical

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“…In [ 81 , 82 ] it is proposed the use of surface normals to improve 3D maps for badly conditioned plane detection. Others, such as [ 83 , 84 ], cope with ToF noisy point clouds using the Iterative Closest Point algorithm to find the relation between two point clouds. Arbeiter [ 85 ] performed an environment reconstruction for a mobile robot combining a ToF camera with two color camera, which is the input for a modified fast-SLAM algorithm.…”

Section: Robot Guidance In Industrial Environmentsmentioning

confidence: 99%

Robot Guidance Using Machine Vision Techniques in Industrial Environments: A Comparative Review

Pérez

Rodríguez

et al. 2016

Sensors

272

122

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In the factory of the future, most of the operations will be done by autonomous robots that need visual feedback to move around the working space avoiding obstacles, to work collaboratively with humans, to identify and locate the working parts, to complete the information provided by other sensors to improve their positioning accuracy, etc. Different vision techniques, such as photogrammetry, stereo vision, structured light, time of flight and laser triangulation, among others, are widely used for inspection and quality control processes in the industry and now for robot guidance. Choosing which type of vision system to use is highly dependent on the parts that need to be located or measured. Thus, in this paper a comparative review of different machine vision techniques for robot guidance is presented. This work analyzes accuracy, range and weight of the sensors, safety, processing time and environmental influences. Researchers and developers can take it as a background information for their future works.

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“…We focus [12] Obstacle avoidance in static env. 3D at high rate SR2 (depth) May et al [13], [14] 3D mapping 3D at high rate/No required Pan-Tilt SR2 (depth) May et al [15] Pose estimation/3D mapping Registered depth-intensity SR3 (depth + intensity) Hedge and Ye [16] Planar feature 3D mapping 3D at high rate/No required Pan-Tilt SR3 Ohno et al [17] 3D mapping 3D at high rate SR2 Stipes et al [18] 3D mapping / Point selection Registered depth-intensity SR3 May et al [19] 3D mapping/SLAM 3D at high rate SR3 Gemeiner et al [20] Corner filtering Registered depth-intensity SR3 (depth + intensity) Thielemann et al [21] Navigation in pipelines 3D allow geometric primitives search SR3 Sheh et al [22] Navigation in hard env. 3D at high rate SR3 + inertial Swadzba et al [23] 3D mapping in dynamic env.…”

Section: Tasksmentioning

confidence: 99%

“…Another adaptation of an ICP-like algorithm for ToF images is presented by Stipes et al [18], where both the depth and the intensity images are used. They present a probabilistic point sampling process to obtain significant points used in the registration process.…”

Section: A Scene-related Tasksmentioning

confidence: 99%