Detection and Compensation of Degeneracy Cases for IMU-Kinect Integrated Continuous SLAM with Plane Features

Cho, HyunGi; Yeon, Suyong; Choi, Hong Soo; Doh, Nakju Lett

doi:10.3390/s18040935

Cited by 16 publications

(5 citation statements)

References 26 publications

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“…Kinect from Microsoft (Redmond, Wa, USA) in the v2 version (Xbox One) is the successor of Kinect v1 (Xbox 360). Due to the price and opportunities offered (sensor set: RGB camera, depths, directional microphones— Figure 2 a), similarly to the previous version, it is very popular: It has found a wide application in various types of applications related to, among others, object recognition and reconstruction, 3D reconstruction and many others [ 53 , 54 , 55 ]. In the case of human recognition based on gait, it significantly expanded the approach area in methods based on a model description (model-based approaches) [ 56 , 57 , 58 , 59 ].…”

Section: Methodsmentioning

confidence: 99%

Recognition of a Person Wearing Sport Shoes or High Heels through Gait Using Two Types of Sensors

Derlatka

Bogdan

2018

Sensors

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Biometrics is currently an area that is both very interesting as well as rapidly growing. Among various types of biometrics the human gait recognition seems to be one of the most intriguing. However, one of the greatest problems within this field of biometrics is the change in gait caused by footwear. A change of shoes results in a significant lowering of accuracy in recognition of people. The following work presents a method which uses data gathered by two sensors: force plates and Microsoft Kinect v2 to reduce this problem. Microsoft Kinect is utilized to measure the body height of a person which allows the reduction of the set of recognized people only to those whose height is similar to that which has been measured. The entire process is preceded by identifying the type of footwear which the person is wearing. The research was conducted on data obtained from 99 people (more than 3400 strides) and the proposed method allowed us to reach a Correct Classification Rate (CCR) greater than 88% which, in comparison to earlier methods reaching CCR’s of <80%, is a significant improvement. The work presents advantages as well as limitations of the proposed method.

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Section: Methodsmentioning

confidence: 99%

Recognition of a Person Wearing Sport Shoes or High Heels through Gait Using Two Types of Sensors

Derlatka

Bogdan

2018

Sensors

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“…The work in [41] proposes to use the observability Gramian as a measure of insufficient sensor measurements required to constrain the optimization. Similarly, [29], [42], [43] proposed to use the condition number of the optimization Hessian as a single combined degeneracy metric for all 6-DoF of the pose estimation optimization problem. With similar reasoning, [44], [45] proposed to utilize the determinant of the fisher information matrix as the degeneracy detection metric instead.…”

Section: B Degeneracy Detectionmentioning

confidence: 99%

X-ICP: Localizability-Aware LiDAR Registration for Robust Localization in Extreme Environments

Tuna,

Nubert,

Nava

et al. 2024

IEEE Trans. Robot.

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Modern robotic systems are required to operate in challenging environments, which demand reliable localization under challenging conditions. LiDAR-based localization methods, such as the Iterative Closest Point (ICP) algorithm, can suffer in geometrically uninformative environments that are known to deteriorate point cloud registration performance and push optimization toward divergence along weakly constrained directions. To overcome this issue, this work proposes i) a robust fine-grained localizability detection module, and ii) a localizability-aware constrained ICP optimization module, which couples with the localizability detection module in a unified manner. The proposed localizability detection is achieved by utilizing the correspondences between the scan and the map to analyze the alignment strength against the principal directions of the optimization as part of its fine-grained LiDAR localizability analysis. In the second part, this localizability analysis is then integrated into the scan-to-map point cloud registration to generate drift-free pose updates by enforcing controlled updates or leaving the degenerate directions of the optimization unchanged. The proposed method is thoroughly evaluated and compared to state-of-the-art methods in simulated and real-world experiments 1 , demonstrating the performance and reliability improvement in LiDAR-challenging environments. In all experiments, the proposed framework demonstrates accurate and generalizable localizability detection and robust pose estimation without environment-specific parameter tuning.

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“…Ref. [ 4 ] addresses the degeneracy problem of the IMU-Kinect sensor utilizing the indoor plane features from the camera. These Kinect-based approaches either work indoors or require a structured environment.…”

Section: Related Workmentioning

confidence: 99%

“…Due to the lightweight and rich information, the camera-based system has been actively researched for small-scale aerial vehicles. In particular, affordable, consumer-grade RGB-D cameras (providing color and depth, such as Microsoft Kinect and RealSense) have enabled considerable advancement for 3D reconstruction and SLAM odometry navigation [ 1 , 2 , 3 , 4 ]. Although quite successful, most current applications have been limited to indoor scenarios due to the limited sensing range and depth dropout problems.…”

Section: Introductionmentioning

confidence: 99%

Depth-Camera-Aided Inertial Navigation Utilizing Directional Constraints

Qayyum

Kim

2021

Sensors

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This paper presents a practical yet effective solution for integrating an RGB-D camera and an inertial sensor to handle the depth dropouts that frequently happen in outdoor environments, due to the short detection range and sunlight interference. In depth drop conditions, only the partial 5-degrees-of-freedom pose information (attitude and position with an unknown scale) is available from the RGB-D sensor. To enable continuous fusion with the inertial solutions, the scale ambiguous position is cast into a directional constraint of the vehicle motion, which is, in essence, an epipolar constraint in multi-view geometry. Unlike other visual navigation approaches, this can effectively reduce the drift in the inertial solutions without delay or under small parallax motion. If a depth image is available, a window-based feature map is maintained to compute the RGB-D odometry, which is then fused with inertial outputs in an extended Kalman filter framework. Flight results from the indoor and outdoor environments, as well as public datasets, demonstrate the improved navigation performance of the proposed approach.

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Detection and Compensation of Degeneracy Cases for IMU-Kinect Integrated Continuous SLAM with Plane Features

Cited by 16 publications

References 26 publications

Recognition of a Person Wearing Sport Shoes or High Heels through Gait Using Two Types of Sensors

Recognition of a Person Wearing Sport Shoes or High Heels through Gait Using Two Types of Sensors

X-ICP: Localizability-Aware LiDAR Registration for Robust Localization in Extreme Environments

Depth-Camera-Aided Inertial Navigation Utilizing Directional Constraints

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