Laser Map Aided Visual Inertial Localization in Changing Environment

2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

et al. 2019

Self Cite

Visual localization has attracted considerable attention due to its low-cost and stable sensor, which is desired in many applications, such as autonomous driving, inspection robots and unmanned aerial vehicles. However, current visual localization methods still struggle with environmental changes across weathers and seasons, as there is significant appearance variation between the map and the query image. The crucial challenge in this situation is that the percentage of outliers, i.e. incorrect feature matches, is high. In this paper, we derive minimal closed form solutions for 3D-2D localization with the aid of inertial measurements, using only 2 point matches or 1 point match and 1 line match. These solutions are further utilized in the proposed 2-entity RANSAC, which is more robust to outliers as both line and point features can be used simultaneously and the number of matches required for pose calculation is reduced. Furthermore, we introduce three feature sampling strategies with different advantages, enabling an automatic selection mechanism. With the mechanism, our 2-entity RANSAC can be adaptive to the environments with different distribution of feature types in different segments. Finally, we evaluate the method on both synthetic and realworld datasets, validating its performance and effectiveness in inter-session scenarios.

Section: Introductionmentioning

confidence: 99%

2-Entity RANSAC for robust visual localization in changing environment

Jiao

2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

et al. 2019

Self Cite

“…4. We utilize the same laser map as in [14] for visual localization which was built based on 21 sessions of data collected along almost the same route within three days in early spring, 2017. The data used for localization was collected in summer and winter with a MTi 100 IMU and a pair of Pointgrey stereo camera.…”

Section: Resultsmentioning

confidence: 99%

Communication constrained cloud-based long-term visual localization in real time

Ding

2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

Tang

et al. 2019

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Visual localization is one of the primary capabilities for mobile robots. Long-term visual localization in real time is particularly challenging, in which the robot is required to efficiently localize itself using visual data where appearance may change significantly over time. In this paper, we propose a cloud-based visual localization system targeting at long-term localization in real time. On the robot, we employ two estimators to achieve accurate and real-time performance. One is a sliding-window based visual inertial odometry, which integrates constraints from consecutive observations and selfmotion measurements, as well as the constraints induced by localization on the cloud. This estimator builds a local visual submap as the virtual observation which is then sent to the cloud as new localization constraints. The other one is a delayed state Extended Kalman Filter to fuse the pose of the robot localized from the cloud, the local odometry and the highfrequency inertial measurements. On the cloud, we propose a longer sliding-window based localization method to aggregate the virtual observations for larger field of view, leading to more robust alignment between virtual observations and the map. Under this architecture, the robot can achieve drift-free and real-time localization using onboard resources even in a network with limited bandwidth, high latency and existence of package loss, which enables the autonomous navigation in real-world environment. We evaluate the effectiveness of our system on a dataset with challenging seasonal and illuminative variations. We further validate the robustness of the system under challenging network conditions.

“…For example, the fusion of the range sensor and the camera can improve the accuracy of object detection [6]. What's more, heterogeneous localization methods, such as visual localization on a laser map [7], can enable low-cost and long-term localization. The precondition of all the above algorithms is the calibration of different sensors, and to that end, we focus on extrinsic calibration of the LiDAR and camera in this work.…”

Section: Lidarcamera Calibration Under Arbitrary Configurations: Obsementioning

confidence: 99%

“…where m is the number of landmarks, [27], our error-state transition matrix has only three variables. The measurement error in general SLAM system at time t k for landmark feature i is the feature reprojection measurement error h proj according to (7):…”

Section: A Observability Of Standard Slammentioning

confidence: 99%

LiDAR-Camera Calibration Under Arbitrary Configurations: Observability and Methods

IEEE Trans. Instrum. Meas.

Ding

et al. 2020

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LiDAR-camera calibration is a precondition for many heterogeneous systems that fuse data from LiDAR and camera. However, the constraint from common field of view and the requirement for strict time synchronization make the calibration a challenging problem. In this paper, we propose a novel LiDAR-camera calibration method aiming to eliminate these two constraints. Specifically, we capture a scan of 3D LiDAR when both the environment and the sensors are stationary, then move the camera to reconstruct the 3D environment using the sequentially obtained images. Finally, we align 3D visual points to the laser scan based on tightly couple graph optimization method to calculate the extrinsic parameters between LiDAR and camera. Under this design, the configuration of these two sensors are free from the common field of view constraint owing to the extended view from the moving camera. And we also eliminate the requirement for strict time synchronization as we only use the single scan of laser data when the sensors are stationary. We theoretically derive the conditions of minimal observability for our method and prove that the accuracy of calibration is improved by collecting more observations from multiple scattered calibration targets. We validate our method on both simulation platform and real-world datasets. Experiments show that our method achieves higher accuracy than other comparable methods, which is in accordance with our theoretical analysis. In addition, the proposed method is beneficial to not only plane measurement error based chessboard, but also other point measurement error based calibration targets, such as boxes and polygonal boards.