Sensor Fusion for Robotic Workspace State Estimation

Olofsson, Björn; Antonsson, Jacob; Kortier, H.G.; Bernhardsson, Bo; Robertsson, Anders; Johansson, Rolf

doi:10.1109/tmech.2015.2506041

Cited by 31 publications

(15 citation statements)

References 30 publications

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“…Again, since the lidar data are motion-distorted, we need to interpolate the pose for each point measurement. We argue that the WNOJ prior (42) offers a more suitable interpolation scheme than the WNOA prior (23). Results for the U of T dataset achieved a greater reduction in error from using the WNOJ prior than the KITTI dataset, which makes no use of the interpolation scheme.…”

Section: B University Of Toronto Datasetmentioning

confidence: 92%

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A White-Noise-on-Jerk Motion Prior for Continuous-Time Trajectory Estimation on <italic>SE(3)</italic>

Tang

Yoon

Barfoot

2019

IEEE Robot. Autom. Lett.

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Simultaneous trajectory estimation and mapping (STEAM) offers an efficient approach to continuous-time trajectory estimation, by representing the trajectory as a Gaussian process (GP). Previous formulations of the STEAM framework use a GP prior that assumes white-noise-on-acceleration, with the prior mean encouraging constant body-centric velocity. We show that such a prior cannot sufficiently represent trajectory sections with non-zero acceleration, resulting in a bias to the posterior estimates.This paper derives a novel motion prior that assumes whitenoise-on-jerk, where the prior mean encourages constant bodycentric acceleration. With the new prior, we formulate a variation of STEAM that estimates the pose, body-centric velocity, and body-centric acceleration. By evaluating across several datasets, we show that the new prior greatly outperforms the white-noiseon-acceleration prior in terms of solution accuracy.

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Section: B University Of Toronto Datasetmentioning

confidence: 92%

“…Again, if we assume that˙ i =˙ i+1 = 0, and J −1 i+1,i i+1 ≈ i+1 , the terms with coefficients Λ 13 and Ω 13 become zeros. Similar to the case with the prior error term, we can essentially recover the pose interpolation equation for the WNOA prior as in (23).…”

Section: B Querying the Trajectorymentioning

confidence: 99%

A White-Noise-on-Jerk Motion Prior for Continuous-Time Trajectory Estimation on <italic>SE(3)</italic>

Tang

Yoon

Barfoot

2019

IEEE Robot. Autom. Lett.

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“…There are many popular techniques in filter-based methods, such as the Extended Kalman filter [28] and Particle Filters [29]. The differences between these methods mainly focus on sensors, dynamic modes and state-estimation algorithms [30]. However, the main drawback is that the filtering strategy updates probability distributions through time without the convergence guarantee, and suffers from computational complexity or large amounts of particles [31].…”

Section: Related Workmentioning

confidence: 99%

Integrate Point-Cloud Segmentation with 3D LiDAR Scan-Matching for Mobile Robot Localization and Mapping

et al. 2019

Sensors

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Localization and mapping are key requirements for autonomous mobile systems to perform navigation and interaction tasks. Iterative Closest Point (ICP) is widely applied for LiDAR scan-matching in the robotic community. In addition, the standard ICP algorithm only considers geometric information when iteratively searching for the nearest point. However, ICP individually cannot achieve accurate point-cloud registration performance in challenging environments such as dynamic environments and highways. Moreover, the computation of searching for the closest points is an expensive step in the ICP algorithm, which is limited to meet real-time requirements, especially when dealing with large-scale point-cloud data. In this paper, we propose a segment-based scan-matching framework for six degree-of-freedom pose estimation and mapping. The LiDAR generates a large number of ground points when scanning, but many of these points are useless and increase the burden of subsequent processing. To address this problem, we first apply an image-based ground-point extraction method to filter out noise and ground points. The point cloud after removing the ground points is then segmented into disjoint sets. After this step, a standard point-to-point ICP is applied into to calculate the six degree-of-freedom transformation between consecutive scans. Furthermore, once closed loops are detected in the environment, a 6D graph-optimization algorithm for global relaxation (6D simultaneous localization and mapping (SLAM)) is employed. Experiments based on publicly available KITTI datasets show that our method requires less runtime while at the same time achieves higher pose estimation accuracy compared with the standard ICP method and its variants.

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“…Olofsson et al [15] discussed the data information and map fusion of two workspaces; the state space is estimated based on EKF and Rao-Blackwellized.…”

Section: If-slam Of Integration Particle Filter Algorithmsmentioning

confidence: 99%

Information-Fusion Methods Based Simultaneous Localization and Mapping for Robot Adapting to Search and Rescue Postdisaster Environments

Wang

Zhang

Song

et al. 2018

Journal of Robotics

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The first application of utilizing unique information-fusion SLAM (IF-SLAM) methods is developed for mobile robots performing simultaneous localization and mapping (SLAM) adapting to search and rescue (SAR) environments in this paper. Several fusion approaches, parallel measurements filtering, exploration trajectories fusing, and combination sensors' measurements and mobile robots' trajectories, are proposed. The novel integration particle filter (IPF) and optimal improved EKF (IEKF) algorithms are derived for information-fusion systems to perform SLAM task in SAR scenarios. The information-fusion architecture consists of multirobots and multisensors (MAM); multiple robots mount on-board laser range finder (LRF) sensors, localization sonars, gyro odometry, Kinect-sensor, RGB-D camera, and other proprioceptive sensors. This information-fusion SLAM (IF-SLAM) is compared with conventional methods, which indicates that fusion trajectory is more consistent with estimated trajectories and real observation trajectories. The simulations and experiments of SLAM process are conducted in both cluttered indoor environment and outdoor collapsed unstructured scenario, and experimental results validate the effectiveness of the proposed informationfusion methods in improving SLAM performances adapting to SAR scenarios.

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Sensor Fusion for Robotic Workspace State Estimation

Cited by 31 publications

References 30 publications

A White-Noise-on-Jerk Motion Prior for Continuous-Time Trajectory Estimation on <italic>SE(3)</italic>

A White-Noise-on-Jerk Motion Prior for Continuous-Time Trajectory Estimation on <italic>SE(3)</italic>

Integrate Point-Cloud Segmentation with 3D LiDAR Scan-Matching for Mobile Robot Localization and Mapping

Information-Fusion Methods Based Simultaneous Localization and Mapping for Robot Adapting to Search and Rescue Postdisaster Environments

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