DLL: Direct LIDAR Localization. A map-based localization approach for aerial robots

Caballero, Fernando; Merino, Luís

doi:10.1109/iros51168.2021.9636501

Cited by 22 publications

(12 citation statements)

References 30 publications

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“…To evaluate the accuracy of the localisation results, the localisation results from the beginning of the first run and the end of the last run, all of which are within the motion capture system region, are used to calculate ATE. We compare our proposed method with SSL‐SLAM [9], AMCL3D [10], and DLL [28]. Besides, we compare our method with ICP and NDT, and the localisation systems based on these two algorithms are implemented using the solution provided by the PCL library [35].…”

Section: Methodsmentioning

confidence: 99%

“…Zuo et al [5,27] reconstruct the semi-dense cloud of the keyframes from the stereo camera and then used the normal distribution transform (NDT)-based method for registration with the prior point cloud map. Inspired by NDT's key idea of modelling the registration process as a non-linear optimisation process, Caballero et al [28] model the point cloud as a distance field for localisation. Compared to the optimisation approaches above, the Bayes filter-based methods, especially MCL, are robust to noise so that they are used in many situations [29].…”

Section: Related Workmentioning

confidence: 99%

“…Inspired by NDT's key idea of modelling the registration process as a non‐linear optimisation process, Caballero et al. [28] model the point cloud as a distance field for localisation. Compared to the optimisation approaches above, the Bayes filter‐based methods, especially MCL, are robust to noise so that they are used in many situations [29].…”

Section: Related Workmentioning

confidence: 99%

See 2 more Smart Citations

Drift‐free localisation using prior cross‐source map for indoor low‐light environments

Tao

Dai

Kong

et al. 2023

IET Cyber-Syst and Robotics

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In this study, a localisation system without cumulative errors is proposed. First, depth odometry is achieved only by using the depth information from the depth camera. Then the point cloud cross-source map registration is realised by 3D particle filtering to obtain the pose of the point cloud relative to the map. Furthermore, we fuse the odometry results with the point cloud to map registration results, so the system can operate effectively even if the map is incomplete. The effectiveness of the system for long-term localisation, localisation in the incomplete map, and localisation in low light through multiple experiments on the self-recorded dataset is demonstrated. Compared with other methods, the results are better than theirs and achieve high indoor localisation accuracy.

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Section: Methodsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

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Drift‐free localisation using prior cross‐source map for indoor low‐light environments

Tao

Dai

Kong

et al. 2023

IET Cyber-Syst and Robotics

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show abstract

“…We compare our results to methods based on monocular camera (VINS‐Mono; Qin et al, 2018), stereo‐camera (VINS‐Stereo; Qin et al, 2019), LiDAR (LOAM; Zhang & Singh, 2014), LiDAR and Camera (LVI‐SAM; Shan et al, 2021), a map‐based localization method (DLL; Caballero & Merino, 2021), and our method (SPINS). The iterative closest point (ICP) method by registering LiDAR scans to point cloud map is also provided as a localization benchmark.…”

Section: Experiments Evaluationmentioning

confidence: 99%

SPINS: A structure priors aided inertial navigation system

Lyu

Liu

Cao

et al. 2023

Journal of Field Robotics

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We propose a navigation system combining sensor‐aided inertial navigation and prior‐map‐based localization to improve the stability and accuracy of robot localization in structure‐rich environments. Specifically, we adopt point, line, and plane features in the navigation system to enhance the feature richness in low‐texture environments and improve the localization reliability. We additionally integrate structure prior information of the environments to constrain the localization drifts and improve the accuracy. The prior information is called structure priors and parameterized as low‐dimensional relative distances/angles between different geometric primitives. The localization is formulated as a graph‐based optimization problem that contains sliding‐window‐based variables and factors, including Inertial Measurement Unit, heterogeneous features, and structure priors. A limited number of structure priors are selected based on the information gain to alleviate the computation burden. Finally, the proposed framework is extensively tested on synthetic data, public data sets, and, more importantly, on the real Unmanned Aerial Vehicle flight data obtained from both indoor and outdoor inspection tasks. The results show that the proposed scheme can effectively improve the accuracy and robustness of localization for autonomous robots in civilian applications.

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“…Our proposal employs measurement model optimization that estimates a pose maximizing the measurement model using a distance field. The similar method is presented by Caballero and Merino [12]. In their method, the summation of distances obtained from the distance field is minimized.…”

Section: Related Workmentioning

confidence: 99%

Hybrid Localization using Model- and Learning-Based Methods: Fusion of Monte Carlo and E2E Localizations via Importance Sampling

Akai

Hirayama

Murase

2020

2020 IEEE International Conference on Robotics and Automation (ICRA)

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This paper presents an efficient solution to 3D-LiDAR-based Monte Carlo localization (MCL). MCL robustly works if particles are exactly sampled around the ground truth. An inertial navigation system (INS) can be used for accurate sampling, but many particles are still needed to be used for solving the 3D localization problem even if INS is available. In particular, huge number of particles are necessary if INS is not available and it makes infeasible to perform 3D MCL in terms of the computational cost. Scan matching (SM), that is optimization-based localization, efficiently works even though INS is not available because SM can ignore movement constraints of a robot and/or device in its optimization process. However, SM sometimes determines an infeasible estimate against movement. We consider that MCL and SM have complemental advantages and disadvantages and propose a fusion method of MCL and SM. Because SM is considered as optimization of a measurement model in terms of the probabilistic modeling, we perform measurement model optimization as SM. The optimization result is then used to approximate the measurement model distribution and the approximated distribution is used to sample particles. The sampled particles are fused with MCL via importance sampling. As a result, the advantages of MCL and SM can be simultaneously utilized while mitigating their disadvantages. Experiments are conducted on the KITTI dataset and other two open datasets. Results show that the presented method can be run on a single CPU thread and accurately perform localization even if INS is not available.

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DLL: Direct LIDAR Localization. A map-based localization approach for aerial robots

Cited by 22 publications

References 30 publications

Drift‐free localisation using prior cross‐source map for indoor low‐light environments

Drift‐free localisation using prior cross‐source map for indoor low‐light environments

SPINS: A structure priors aided inertial navigation system

Hybrid Localization using Model- and Learning-Based Methods: Fusion of Monte Carlo and E2E Localizations via Importance Sampling

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