Automatic calibration and registration of lidar and stereo camera without calibration objects

John, Vijay; Qian, Long; Liu, Zheng; Mita, Seiichi

doi:10.1109/icves.2015.7396923

Cited by 11 publications

(6 citation statements)

References 14 publications

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“…Through the years, interest in combining various sensors to achieve higher accuracy and efficiency has been widespread. Many studies regarding sensor fusions have been successfully integrated and applied in multiple fields, such as camera-lidar integration for semantic mapping [ 47 ], driver aid systems for intelligent vehicles [ 48 , 49 ], target tracking for robotic fish [ 50 ], activity detection of sound sources [ 51 ] and avian monitoring [ 52 ]. An underwater acoustic-optic image matching was proposed by Zhou et al [ 53 ].…”

Section: Related Workmentioning

confidence: 99%

A Two-Mode Underwater Smart Sensor Object for Precision Aquaculture Based on AIoT Technology

Chang

Ubina

Cheng

et al. 2022

Sensors

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Monitoring the status of culture fish is an essential task for precision aquaculture using a smart underwater imaging device as a non-intrusive way of sensing to monitor freely swimming fish even in turbid or low-ambient-light waters. This paper developed a two-mode underwater surveillance camera system consisting of a sonar imaging device and a stereo camera. The sonar imaging device has two cloud-based Artificial Intelligence (AI) functions that estimate the quantity and the distribution of the length and weight of fish in a crowded fish school. Because sonar images can be noisy and fish instances of an overcrowded fish school are often overlapped, machine learning technologies, such as Mask R-CNN, Gaussian mixture models, convolutional neural networks, and semantic segmentation networks were employed to address the difficulty in the analysis of fish in sonar images. Furthermore, the sonar and stereo RGB images were aligned in the 3D space, offering an additional AI function for fish annotation based on RGB images. The proposed two-mode surveillance camera was tested to collect data from aquaculture tanks and off-shore net cages using a cloud-based AIoT system. The accuracy of the proposed AI functions based on human-annotated fish metric data sets were tested to verify the feasibility and suitability of the smart camera for the estimation of remote underwater fish metrics.

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Section: Related Workmentioning

confidence: 99%

A Two-Mode Underwater Smart Sensor Object for Precision Aquaculture Based on AIoT Technology

Chang

Ubina

Cheng

et al. 2022

Sensors

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show abstract

“…To build the KITTI dataset [23], Geiger et al calibrated LiDAR and cameras using multiple calibration boards in a controlled garage environment [24] followed by the manual selection of corresponding points. Although marker-less calibration methods have been examined [25], [26], [27], it remains difficult to stably realize accurate extrinsic calibration in uncontrolled environments.…”

Section: Introductionmentioning

confidence: 99%

Non-learning Stereo-aided Depth Completion under Mis-projection via Selective Stereo Matching

Yao,

Ishikawa,

Ando

et al. 2022

Preprint

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We propose a non-learning depth completion method for a sparse depth map captured using a light detection and ranging (LiDAR) sensor guided by a pair of stereo images. Generally, conventional stereo-aided depth completion methods have two limiations. (i) They assume the given sparse depth map is accurately aligned to the input image, whereas the alignment is difficult to achieve in practice. (ii) They have limited accuracy in the long range because the depth is estimated by pixel disparity. To solve the abovementioned limitations, we propose selective stereo matching (SSM) that searches the most appropriate depth value for each image pixel from its neighborly projected LiDAR points based on an energy minimization framework. This depth selection approach can handle any type of mis-projection. Moreover, SSM has an advantage in terms of long-range depth accuracy because it directly uses the LiDAR measurement rather than the depth acquired from the stereo. SSM is a discrete process; thus, we apply variational smoothing with binary anisotropic diffusion tensor (B-ADT) to generate a continuous depth map while preserving depth discontinuity across object boundaries. Experimentally, compared with the previous state-of-the-art stereo-aided depth completion, the proposed method reduced the mean absolute error (MAE) of the depth estimation to 0.65 times and demonstrated approximately twice more accurate estimation in the long range. Moreover, under various LiDAR-camera calibration errors, the proposed method reduced the depth estimation MAE to 0.34-0.93 times from previous depth completion methods.

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“…To build the KITTI dataset [23], Geiger et al calibrated LiDAR and cameras using multiple calibration boards in a controlled garage environment [24] followed by the manual selection of corresponding points. Although marker-less calibration methods have been examined [25]- [27], it remains difficult to stably realize accurate extrinsic calibration in uncontrolled environments.…”

Section: Introductionmentioning

confidence: 99%

Non-Learning Stereo-Aided Depth Completion Under Mis-Projection via Selective Stereo Matching

Yao¹,

Ishikawa

Ando

et al. 2021

IEEE Access

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We propose a non-learning depth completion method for a sparse depth map captured using a light detection and ranging (LiDAR) sensor guided by a pair of stereo images. Generally, conventional stereo-aided depth completion methods have two limiations. (i) They assume the given sparse depth map is accurately aligned to the input image, whereas the alignment is difficult to achieve in practice.(ii) They have limited accuracy in the long range because the depth is estimated by pixel disparity. To solve the abovementioned limitations, we propose selective stereo matching (SSM) that searches the most appropriate depth value for each image pixel from its neighborly projected LiDAR points based on an energy minimization framework. This depth selection approach can handle any type of mis-projection. Moreover, SSM has an advantage in terms of long-range depth accuracy because it directly uses the LiDAR measurement rather than the depth acquired from the stereo. SSM is a discrete process; thus, we apply variational smoothing with binary anisotropic diffusion tensor (B-ADT) to generate a continuous depth map while preserving depth discontinuity across object boundaries. Experimentally, compared with the previous state-of-the-art stereo-aided depth completion, the proposed method reduced the mean absolute error (MAE) of the depth estimation to 0.65 times and demonstrated approximately twice more accurate estimation in the long range. Moreover, under various LiDAR-camera calibration errors, the proposed method reduced the depth estimation MAE to 0.34-0.93 times from previous depth completion methods.

show abstract

Automatic calibration and registration of lidar and stereo camera without calibration objects

Cited by 11 publications

References 14 publications

A Two-Mode Underwater Smart Sensor Object for Precision Aquaculture Based on AIoT Technology

A Two-Mode Underwater Smart Sensor Object for Precision Aquaculture Based on AIoT Technology

Non-learning Stereo-aided Depth Completion under Mis-projection via Selective Stereo Matching

Non-Learning Stereo-Aided Depth Completion Under Mis-Projection via Selective Stereo Matching

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