| 1. |
- Callmer, Jonas, 1981-, et al.
(författare)
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Tree of Words for Visual Loop Closure Detection in Urban SLAM
- 2008
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Ingår i: Proceedings of the '08 Australasian Conference on Robotics and Automation. - 9780646506432 ; , s. 102-
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Konferensbidrag (refereegranskat)abstract
- This paper introduces vision based loop closure detection in Simultaneous Localisation And Mapping (SLAM) using Tree of Words. The loop closure performance in a complex urban environment is examined and an additional feature is suggested for safer matching. A SLAM ground experiment in an urban area is performed using Tree of Words, a delayed state information filter and planar laser scans for relative pose estimation. Results show that a good map estimation using our vision based loop closure detection can be obtained in near real, yet constant, time. It is shown that an odometry supported recall rate of almost 70% can be obtained with a false detection rate of about 0.01%.
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| 2. |
- Granström, Karl, 1981-, et al.
(författare)
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Learning to Detect Loop Closure from Range Data
- 2009
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Ingår i: Proceedings of '09 IEEE International Conference on Robotics and Automation. - 9781424427895 ; , s. 15-22
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Konferensbidrag (refereegranskat)abstract
- Despite significant developments in the Simultaneous Localisation and Mapping (SLAM) problem, loop closure detection is still challenging in large scale unstructured environments. Current solutions rely on heuristics that lack generalisation properties, in particular when range sensors are the only source of information about the robot's surrounding environment. This paper presents a machine learning approach for the loop closure detection problem using range sensors. A binary classifier based on boosting is used to detect loop closures. The algorithm performs robustly, even under potential occlusions and significant changes in rotation and translation. We developed a number of features, extracted from range data, that are invariant to rotation. Additionally, we present a general framework for scan-matching SLAM in outdoor environments. Experimental results in large scale urban environments show the robustness of the approach, with a detection rate of 85% and a false alarm rate of only 1%. The proposed algorithm can be computed in real-time and achieves competitive performance with no manual specification of thresholds given the features.
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| 3. |
- Xia, Yuxuan, 1993, et al.
(författare)
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Extended Object Tracking with Automotive Radar Using Learned Structural Measurement Model
- 2020
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Ingår i: IEEE National Radar Conference - Proceedings. - 1097-5659.
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Konferensbidrag (refereegranskat)abstract
- This paper presents a data-driven measurement model for extended object tracking (EOT) with automotive radar. Specifically, the spatial distribution of automotive radar measurements is modeled as a hierarchical truncated Gaussian with structural geometry parameters (e.g., truncation bounds, their orientation, and a scaling factor) learned from the training data. The contribution is twofold. First, the learned measurement model can provide an adequate resemblance to the spatial distribution of real-world automotive radar measurements. Second, large-scale offline training datasets can be leveraged to learn the geometry-related parameters and offload the computationally demanding model parameter estimation from the state update step. The learned structural measurement model is further incorporated into the random matrix-based EOT approach with a new state update step. The effectiveness of the proposed approach is verified on the nuScenes dataset.
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| 4. |
- Xia, Yuxuan, 1993, et al.
(författare)
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Learning-Based Extended Object Tracking Using Hierarchical Truncation Measurement Model With Automotive Radar
- 2021
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Ingår i: IEEE Journal on Selected Topics in Signal Processing. - 1941-0484 .- 1932-4553. ; 15:4, s. 1013-1029
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Tidskriftsartikel (refereegranskat)abstract
- This paper presents a data-driven measurement model for extended object tracking (EOT) with automotive radar. Specifically, the spatial distribution of automotive radar measurements is modeled as a hierarchical truncated Gaussian (HTG) with structural geometry parameters that can be learned from the training data. The HTG measurement model provides an adequate resemblance to the spatial distribution of real-world automotive radar measurements. Moreover, large-scale radar datasets can be leveraged to learn the geometry-related model parameters and offload the computationally demanding model parameter estimation from the state update step. The learned HTG measurement model is further incorporated into a random matrix based EOT approach with two (multi-sensor) measurement updates: one is based on a factorized Gaussian inverse-Wishart density representation and the other is based on a Rao-Blackwellized particle density representation. The effectiveness of the proposed approaches is verified on both synthetic data and real-world nuScenes dataset over 300 trajectories.
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| 5. |
- Beard, M., et al.
(författare)
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Multiple Extended Target Tracking With Labeled Random Finite Sets
- 2016
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Ingår i: IEEE Transactions on Signal Processing. - : Institute of Electrical and Electronics Engineers (IEEE). - 1941-0476 .- 1053-587X. ; 64:7, s. 1638-1653
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Tidskriftsartikel (refereegranskat)abstract
- Targets that generate multiple measurements at a given instant in time are commonly known as extended targets. These present a challenge for many tracking algorithms, as they violate one of the key assumptions of the standard measurement model. In this paper, a new algorithm is proposed for tracking multiple extended targets in clutter, which is capable of estimating the number of targets, as well the trajectories of their states, comprising the kinematics, measurement rates, and extents. The proposed technique is based on modeling the multi-target state as a generalized labeled multi-Bernoulli (GLMB) random finite set (RFS), within which the extended targets are modeled using gamma Gaussian inverse Wishart (GGIW) distributions. A cheaper variant of the algorithm is also proposed, based on the labelled multi-Bernoulli (LMB) filter. The proposed GLMB/LMB-based algorithms are compared with an extended target version of the cardinalized probability hypothesis density (CPHD) filter, and simulation results show that the (G) LMB has improved estimation and tracking performance.
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| 6. |
- Edman, Viktor, et al.
(författare)
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Pedestrian Group Tracking Using the GM-PHD Filter
- 2013
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Ingår i: Proceedings of the 21st European Signal Processing Conference.
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Konferensbidrag (övrigt vetenskapligt/konstnärligt)abstract
- A GM-PHD filter is used for pedestrian tracking in a crowdsurveillance application. The purpose is to keep track of thedifferent groups over time as well as to represent the shape ofthe groups and the number of people within the groups. In-put data to the GM-PHD filter are detections using a state ofthe art algorithm applied to video frames from the PETS 2012benchmark data. In a first step, the detections in the framesare converted from image coordinates to world coordinates.This implies that groups can be defined in physical units interms of distance in meters and speed differences in metersper second. The GM-PHD filter is a Bayesian framework thatdoes not form tracks of individuals. Its output is well suitedfor clustering of individuals into groups. The results demon-strate that the GM-PHD filter has the capability of estimatingthe correct number of groups with an accurate representationof their sizes and shapes.
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| 7. |
- Fatemi, Maryam, 1982, et al.
(författare)
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Poisson multi-Bernoulli filter for extended object tracking
- 2016
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Tidskriftsartikel (övrigt vetenskapligt/konstnärligt)abstract
- This paper presents a track-oriented Poisson multi-Bernoulli(PMB) filter for extended objects. The PMB filter is based onthe Poisson multi-Bernoulli mixture (PMBM) conjugate priorand approximates the posterior PMBM by merging tracks acrossdata association hypotheses. A method to create new tracks ina reasonable manner is proposed which uses a combination ofpre-clustering, recycling and equivalence class among PMBMdistributions.To approximate the marginal distributions of different tracks we use Gibbs sampling. The performance of thePMB is compared to the PMBM using a simulated scenario.
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| 8. |
- Fatemi, Maryam, 1982, et al.
(författare)
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Poisson Multi-Bernoulli Mapping Using Gibbs Sampling
- 2017
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Ingår i: IEEE Transactions on Signal Processing. - : Institute of Electrical and Electronics Engineers (IEEE). - 1941-0476 .- 1053-587X. ; 65:11, s. 2814-2827
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Tidskriftsartikel (refereegranskat)abstract
- This paper addresses the mapping problem. Using a conjugate prior form, we derive the exact theoretical batch multiobject posterior density of the map given a set of measurements. The landmarks in the map are modeled as extended objects, and the measurements are described as a Poisson process, conditioned on the map. We use a Poisson process prior on the map and prove that the posterior distribution is a hybrid Poisson, multi-Bernoulli mixture distribution. We devise a Gibbs sampling algorithm to sample from the batch multiobject posterior. The proposed method can handle uncertainties in the data associations and the cardinality of the set of landmarks, and is parallelizable, making it suitable for large-scale problems. The performance of the proposed method is evaluated on synthetic data and is shown to outperform a state-of-the-art method.
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| 9. |
- Fatemi, Maryam, 1982, et al.
(författare)
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Poisson Multi-Bernoulli Radar Mapping Using Gibbs Sampling
- 2016
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Tidskriftsartikel (övrigt vetenskapligt/konstnärligt)abstract
- This paper addresses the radar mapping problem.Using a conjugate prior form, we derive the exact theoretical batch multi-object posterior density of the map given a set of measurements. The landmarks in the map are modeled as extended objects, and the measurements are described as a Poisson process, conditioned on the map. We use a Poisson process prior on the map and prove that the posterior distribution is a hybrid Poisson, multi-Bernoulli mixture distribution. We devise a Gibbs sampling algorithm to sample from the batch multi-object posterior. The proposed method can handle uncertainties in thedata associations and the cardinality of the set of landmarks, and is parallelizable, making it suitable for large-scale problems. The performance of the proposed method is evaluated on synthetic data and is shown to outperform an state-of-the-art method.
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| 10. |
- Fröhle, Markus, 1984, et al.
(författare)
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Decentralized Poisson Multi-Bernoulli Filtering for Vehicle Tracking
- 2020
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Ingår i: IEEE Access. - 2169-3536 .- 2169-3536. ; 8, s. 126414-126427
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Tidskriftsartikel (refereegranskat)abstract
- A decentralized Poisson multi-Bernoulli filter is proposed to track multiple vehicles using multiple high-resolution sensors. Independent filters estimate the vehicles' presence, state, and shape using a Gaussian process extent model; a decentralized filter is realized through fusion of the filters posterior densities. An efficient implementation is achieved by parametric state representation, utilization of single hypothesis tracks, and fusion of vehicle information based on a fusion mapping. Numerical results demonstrate the performance.
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