Shaving Aided Interval Constraint Propagation for Guaranteed Landmark Estimation

Landmark depth estimation is a key step in simultaneous localization and mapping (SLAM) algorithm. Recently, interval analysis-based methods have aroused increasing interests from researchers due to its capability of providing guaranteed solution. However, they cannot contract the infinite depth unc...

Full description

Saved in:
Bibliographic Details
Published in2023 8th International Conference on Intelligent Computing and Signal Processing (ICSP) pp. 447 - 451
Main Authors Wang, Zhan, Lu, Ding, Chen, Jiemin, Zhang, Yingchi, Yu, Rongdong, Meng, Yuwei
Format Conference Proceeding
LanguageEnglish
Published IEEE 21.04.2023
Subjects
Azimuth
interval analysis
landmark estimation
locality problem
Numerical models
Signal processing
Signal processing algorithms
Simultaneous localization and mapping
SLAM
Uncertainty
Upper bound
Online AccessGet full text

Cover

More Information
Summary:Landmark depth estimation is a key step in simultaneous localization and mapping (SLAM) algorithm. Recently, interval analysis-based methods have aroused increasing interests from researchers due to its capability of providing guaranteed solution. However, they cannot contract the infinite depth uncertainty in some scenarios when applying local consistent interval constraint propagation (ICP) algorithm. To solve this problem, this paper proposes a shaving aided ICP (SICP) algorithm for guaranteed landmark depth uncertainty contraction. The principle of proposed SICP is to determine the upper and lower bounds of depth uncertainty by recursively calling a local consistent ICP algorithm on the sub-slices of the initial domain and removing those which are contradict to the constraints generated by the observation model. As a result, the landmark uncertainty is efficiently contracted without losing any feasible value. The numerical results show that the infinite depth uncertainty can be directly contracted by our proposed SICP algorithm and achieved better estimation (contraction) than normal ICP algorithm in 4 certain observation cases, showing the potential application of the proposed algorithm in SLAM algorithm.
DOI:10.1109/ICSP58490.2023.10248814