Accuracy bounds and optimal computation of robot localization

Kanatani, Kenichi; Ohta, Nobuhiro

doi:10.1007/pl00013274

Cited by 3 publications

(2 citation statements)

References 23 publications

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“…Sugihara et al [15] and Kanatani et al [16] describe the problem in detail. Suppose we have two landmarks L 1 , L 2 , their projection to the image plane l 1 = { ū1 , ā1 }, l 2 = { ū2 , ā2 } and the intrinsic matrix of our camera.…”

Section: A Translation Calculationmentioning

confidence: 99%

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Coarse-To-Fine Visual Localization Using Semantic Compact Map

Liao¹,

Shi²,

Qi³

et al. 2019

Preprint

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Robust visual localization for urban vehicles remains challenging and unsolved. The limitation of computation efficiency and memory size has made it harder for large-scale applications. Since semantic information serves as a stable and compact representation of the environment, we propose a coarse-to-fine localization system based on a semantic compact map. Pole-like objects are stored in the compact map, then are extracted from semantically segmented images as observations. Localization is performed by a particle filter, followed by a pose alignment module decoupling translation and rotation to achieve better accuracy. We evaluate our system both on synthetic and realistic datasets and compare it with two baselines, a state-of-art semantic feature-based system and a traditional SIFT feature-based system. Experiments demonstrate that even with a significantly small map, such as a 10 KB map for a 3.7 km long trajectory, our system provides a comparable accuracy with the baselines.

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Section: A Translation Calculationmentioning

confidence: 99%

“…After that, we propose the pose alignment module to adjust the pose from the particle filter to a finer position. With accurate landmarks in the compact map, we can use geometry solution that decouples translation and rotation [15], [16] to achieve higher accuracy.…”

Section: Introductionmentioning

confidence: 99%