Robust 2D map building with motion-free ICP algorithm for mobile robot navigation

Hwang, Yo-Seop; Lee, Jang-Myung

doi:10.1017/s0263574716000540

Cited by 6 publications

(3 citation statements)

References 23 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…It is demonstrated using publicly available datasets that the proposed Linear SLAM algorithm can provide accurate SLAM results. For practical applications, the proposed algorithm can be easily combined with other robust local map building algorithms in complex unknown environments such as [23,24] to solve practical large-scale SLAM problems in a more robust manner.…”

Section: Introductionmentioning

confidence: 99%

Linear SLAM: Linearising the SLAM problems using submap joining

2019

View full text Add to dashboard Cite

The main contribution of this paper is a new submap joining based approach for solving large-scale Simultaneous Localization and Mapping (SLAM) problems. Each local submap is independently built using the local information through solving a small-scale SLAM; the joining of submaps mainly involves solving linear least squares and performing nonlinear coordinate transformations. Through approximating the local submap information as the state estimate and its corresponding information matrix, judiciously selecting the submap coordinate frames, and approximating the joining of a large number of submaps by joining only two maps at a time, either sequentially or in a more efficient Divide and Conquer manner, the nonlinear optimization process involved in most of the existing submap joining approaches is avoided. Thus the proposed submap joining algorithm does not require initial guess or iterations since linear least squares problems have closed-form solutions. The proposed Linear SLAM technique is applicable to feature-based SLAM, pose graph SLAM and D-SLAM, in both two and three dimensions, and does not require any assumption on the character of the covariance matrices. Simulations and experiments are performed to evaluate the proposed Linear SLAM algorithm. Results using publicly available datasets in 2D and 3D show that Linear SLAM produces results that are very close to the best solutions that can be obtained using full nonlinear optimization algorithm started from an accurate initial guess. The C/C++ and MATLAB source codes of Linear SLAM are available on OpenSLAM.

show abstract

Section: Introductionmentioning

confidence: 99%

Linear SLAM: Linearising the SLAM problems using submap joining

2019

View full text Add to dashboard Cite

show abstract

“…Localization of the robot, and therefore calibration, can also be based on active or passive beacons, but this kind of solution is not always possible in nonlaboratory or industrial environments. Some studies have developed a calibration method for DDR based mainly on laser range finder sensors [15][16][17]. In the context discussed here, with an absence of precise long range sensors, odometry based on encoder information is the main source of information for localization.…”

Section: Introductionmentioning

confidence: 99%

On-Board Correction of Systematic Odometry Errors in Differential Robots

et al. 2019

View full text Add to dashboard Cite

An easier method for the calibration of differential drive robots is presented. Most calibration is done on-board and it is not necessary to spend too much time taking note of the robot's position. The calibration method does not need a large free space to perform the tests. The bigger space is merely in a straight line, which is easy to find. The results with the method presented are compared with those from UMB for reference, and they show very little deviation while the proposed calibration is much simpler.

show abstract

“…The first set of tests, compared the workability and accuracy between the proposed method and UMBmark techniques, while the second test compared the performance of mobile robot, calibrated by any one of the techniques, while moving on an unknown path. In another related work by Hwang and Lee, 22 laser range finder was used to build a 2-D map using motion-free ICP algorithm. The consecutive data frames acquired from the laser range finder were used to formulate a least square problem, having constrained rotation and translation.…”

Section: Introductionmentioning

confidence: 99%

Maximum clearance rapid motion planning algorithm

Paliwal¹,

Kala²

2018

Robotica

View full text Add to dashboard Cite

SUMMARYThis paper proposes a new path-planning algorithm which is close to the family of bug algorithms. Path planning is one of the challenging problems in the area of service robotics. In practical applications, traditional methods have some limitations with respect to cost, efficiency, security, flexibility, portability, etc. Our proposed algorithm offers a computationally inexpensive goal-oriented strategy by following a smooth and short trajectory. The paper also presents comparisons with other algorithms. In addition, the paper also presents a test bed which is created to test the algorithm. We have used a two-wheeled differential drive robot for the navigation and only a single camera is used as a feedback sensor. Using an extended Kalman filter, we localize the robot efficiently in the map. Furthermore, we compare the actual path, predicted path and planned path to check the effectiveness of the control system.

show abstract

Robust 2D map building with motion-free ICP algorithm for mobile robot navigation

Cited by 6 publications

References 23 publications

Linear SLAM: Linearising the SLAM problems using submap joining

Linear SLAM: Linearising the SLAM problems using submap joining

On-Board Correction of Systematic Odometry Errors in Differential Robots

Maximum clearance rapid motion planning algorithm

Contact Info

Product

Resources

About