Joint Point Cloud and Image Based Localization For Efficient Inspection in Mixed Reality (1811.02563v1)

Abstract: This paper introduces a method of structure inspection using mixed-reality headsets to reduce the human effort in reporting accurate inspection information such as fault locations in 3D coordinates. Prior to every inspection, the headset needs to be localized. While external pose estimation and fiducial marker based localization would require setup, maintenance, and manual calibration; marker-free self-localization can be achieved using the onboard depth sensor and camera. However, due to limited depth sensor range of portable mixed-reality headsets like Microsoft HoloLens, localization based on simple point cloud registration (sPCR) would require extensive mapping of the environment. Also, localization based on camera image would face the same issues as stereo ambiguities and hence depends on viewpoint. We thus introduce a novel approach to Joint Point Cloud and Image-based Localization (JPIL) for mixed-reality headsets that use visual cues and headset orientation to register small, partially overlapped point clouds and save significant manual labor and time in environment mapping. Our empirical results compared to sPCR show average 10 fold reduction of required overlap surface area that could potentially save on average 20 minutes per inspection. JPIL is not only restricted to inspection tasks but also can be essential in enabling intuitive human-robot interaction for spatial mapping and scene understanding in conjunction with other agents like autonomous robotic systems that are increasingly being deployed in outdoor environments for applications like structural inspection.

• The paper presents a novel JPIL method that fuses depth, camera, and IMU data to improve mixed-reality localization accuracy.
• The approach significantly reduces required surface overlap, cutting inspection time by approximately 20 minutes while achieving 0.28m accuracy.
• By integrating a modified 3D point cloud descriptor and a robust matching algorithm, the method effectively tackles stereo ambiguities and sensor errors.

Overview and Implications of Joint Point Cloud and Image Based Localization for Mixed Reality

The paper presented explores an innovative method termed Joint Point Cloud and Image-based Localization (JPIL) aimed at improving inspection tasks utilizing mixed-reality (MR) headsets. The methodology is designed to enhance localization accuracy by leveraging both point cloud and image data, significantly reducing on-site mapping time, which is a crucial factor in industries relying on efficient spatial inspections like construction and infrastructure management.

Methodology and Technical Contributions

JPIL is proposed as a solution to the limitations seen in current marker-free localization methods, particularly in handling stereo ambiguities and extensive environment mapping requirements. The paper highlights the inadequacies of simple Point Cloud Registration (sPCR) in mixed-reality contexts due to extensive overlap needs and, conversely, the challenges faced in purely image-based localization owing to viewpoint dependency.

A novel contribution of the JPIL method includes integrating data from key onboard sensors within MR headsets, such as depth sensors, cameras, and Inertial Measurement Units (IMUs). This sensor fusion supports an enhanced registration process by simultaneously leveraging visual and spatial cues. Specifically, the combination of visual information and headset orientation data enables the registration of small, partially overlapped point clouds with significant efficiency improvements.

Key technical contributions presented in the paper include:

• A modified binary shape context-based 3D point cloud descriptor, which incorporates orientation specificity to improve registration accuracy.
• An efficient multiple-candidate descriptor matching algorithm that tackles the translation symmetry issue by selecting descriptors robustly under low overlap conditions.

Empirical Results and Performance

Through empirical studies, the JPIL approach showcases its capacity to drastically diminish the required surface overlap by an average of 10 fold compared to traditional approaches. The average reduction reported equates to saving roughly 20 minutes per inspection. This efficiency gain represents a meaningful improvement for real-world applications where time and resource constraints are critical.

Precise numerical results indicate that the JPIL method achieves localization accuracies of 0.28 meters, comparable to that of more extensive and resource-intensive techniques. Furthermore, studies demonstrate robustness to sensor error, maintaining effectiveness with orientation errors up to 5 degrees. This resilience positions JPIL as a robust solution in dynamic or sensor-imperfect environments.

Practical and Theoretical Implications

The implications of JPIL are both practical and theoretical. Practically, the method enhances human-machine interaction capabilities within MR contexts, facilitating more intuitive inspection processes by reducing the operational burden on inspectors. The method also holds potential for deployment in autonomous robotic systems, supporting refined spatial mapping and understanding that integrate seamlessly into broader automated workflows.

Theoretically, JPIL contributes to the understanding and development of hybrid localization techniques, blending traditional point cloud methods with advanced image processing and orientation data integration. The introduction of this hybrid model suggests future explorations may further optimize the use of headsets’ diverse sensing capabilities, thereby promoting strategies that are both highly efficient and scalable.

Future Developments and Research Trajectories

While JPIL presents significant advancements, the paper acknowledges areas for future exploration, notably in utilizing 3D information from time-series image data to enhance robustness and operational efficiency further. Future research may also explore adaptive algorithms that dynamically adjust sensor integration based on environmental markers or inspection objectives.

Overall, this paper presents a valuable contribution to mixed-reality localization methodologies, offering both immediate practical benefits and a foundation for ongoing research and innovation in the intersection of spatial computing and augmented reality technologies. As MR continues to evolve, approaches like JPIL signify critical enablers in transforming inspection and interaction processes across various applications.