Motion Estimation of a Mobile Robot Using Different Types of 3D Sensors

Netramai, Chayakorn; Oleksandr, Mochalin; Joochimt, C.; Roth, Hubert

doi:10.1109/icas.2008.39

Cited by 3 publications

(5 citation statements)

References 7 publications

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“…In a preliminary work, Kuhnert and Stommel [27] present a revision of their 3D environment reconstruction algorithm combining information from a stereo system and a ToF sensor. Later, Netramai et al [28] compared the performance of a motion estimation algorithm using both ToF and depth from stereo. They also presented an oversimplified fusion algorithm that relies on the optical calibration of both sensors to solve the correspondence problem.…”

Section: Scene-related Tasksmentioning

confidence: 99%

“…light/texture/shadow independence Canesta Yuan et al [26] Navigation and obst. avoidance Increased detection zone SR3 + laser Kuhnert and Stommel et al [27] 3D reconstruction Easy color registration PMD + stereo Netramai et al [28] Motion estimation 3D at high rate PMD + stereo Huhle et al [29] 3D mapping Easy registration of depth and color PMD + color camera Prusak et al [30] Obst. avoidance/Map building Absolute scale/better pose estimation PMD + spherical camera Swadzba et al [31] 3D mapping/Map optimization 3D at high rate SR3 Vaskevicius et al [32] Localization/Map optimisation Neighbourhood relation of pixels SR3 Poppinga [33] No color restrictions entails a minimum update of their previous laser-scanner-based algorithm.…”

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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“…This algorithm is capable of rendering environment maps. Kuhnert and Netramai [ 86 , 87 ] combined a ToF sensor and a stereo system for environment reconstruction.…”

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

245

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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“…light/texture/shadow independence Canesta Yuan et al [27] Navigation and obst. avoidance Increased detection zone SR3 + laser Kuhnert and Stommel et al [28] 3D reconstruction Easy color registration PMD + stereo Netramai et al [29] Motion estimation 3D at high rate PMD + stereo Huhle et al [30] 3D mapping Easy registration of depth and color PMD + color camera Prusak et al [31] Obst. avoidance/Map building Absolute scale/better pose estimation PMD + spherical camera Swadzba et al [32] 3D mapping/Map optimisation 3D at high rate SR3 Vaskevicius et al [33] Localization/Map optimisation Neighbourhood relation of pixels SR3 Poppinga [34] No color restrictions our review on two complementary areas: scene-related tasks and object-related tasks.…”

Section: Tasksmentioning

confidence: 99%

“…In a preliminary work, Kuhnert and Stommel [28] present a revision of their 3D environment reconstruction algorithm combining information from a stereo system and a ToF sensor. Later, Netramai et al [29] compared the performance of a motion estimation algorithm using both ToF and depth from stereo. They also presented an oversimplified fusion algorithm that relies on the optical calibration of both sensors to solve the correspondence problem.…”

Section: A Scene-related Tasksmentioning

confidence: 99%