Enhancing Exploration Algorithms for Navigation with Visual SLAM

被引：4
|
作者：
Muravyev, Kirill [1 ,2 ]
Bokovoy, Andrey [1 ]
Yakovlev, Konstantin [1 ]
机构：
[1] Russian Acad Sci, Fed Res Ctr Comp Sci & Control, Artificial Intelligence Res Inst, Moscow, Russia
[2] Moscow Inst Phys & Technol, Dolgoprudnyi, Russia
来源：
ARTIFICIAL INTELLIGENCE, RCAI 2021 | 2021年 / 12948卷
关键词：
Exploration; Vision-based simultaneous localization and mapping; Simulation; Robotics; VISION; SYSTEM; SCALE; IMU;
D O I：
10.1007/978-3-030-86855-0_14
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
Exploration is an important step in autonomous navigation of robotic systems. In this paper we introduce a series of enhancements for exploration algorithms in order to use them with vision-based simultaneous localization and mapping (vSLAM) methods. We evaluate developed approaches in photo-realistic simulator in two modes: with ground-truth depths and neural network reconstructed depth maps as vSLAM input. We evaluate standard metrics in order to estimate exploration coverage.
引用
收藏
页码：197 / 212
页数：16
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