Real-Time Slam From RGB-D Data on a Legged Robot: An Experimental Study

Belter, Dominik; Kostusiak, Aleksander; Nowicki, Michał; Skrzypczyński, Piotr

doi:10.1142/9789813149137_0039

Cited by 2 publications

(2 citation statements)

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“…Finally, the graph of connected keyframes is optimized by using the g o library, which is also employed in PUT SLAM. All experiments presented in this paper were performed using the SURF-based variant of RGB-D SLAM v2, as results presented in both Belter et al (2016c) and Cˇ´ızek and Faigl (2016) suggest that with SURF this system yieldsˇ best results even without the GPGPU acceleration. In the g 2 o back-end the PCG solver is used, and the loop closure parameters are set as in Fischer et al (2016).…”

Section: Rgb-d Slam V2mentioning

confidence: 99%

“…This work was then extended to a comparison with the passive-stereo-based S-PTAM system (Fischer et al, 2016). At the same time, Belter et al (2016c) compared four contemporary approaches to localization of a multi-legged robot with the RGB-D sensor. As the need for a common ground for objective SLAM evaluation is acknowledged across the robotics community, we extend in this article the research of Belter et al (2016c) and Cížek and Faigl (2016) to provide a systematic experimental evaluation of the representative SLAM architectures with RGB-D perception deployed on multi-legged robots.…”

Section: Related Workmentioning

confidence: 99%

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An experimental study on feature-based SLAM for multi-legged robots with RGB-D sensors

Nowicki

Belter

Kostusiak

et al. 2017

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Purpose This paper aims to evaluate four different simultaneous localization and mapping (SLAM) systems in the context of localization of multi-legged walking robots equipped with compact RGB-D sensors. This paper identifies problems related to in-motion data acquisition in a legged robot and evaluates the particular building blocks and concepts applied in contemporary SLAM systems against these problems. The SLAM systems are evaluated on two independent experimental set-ups, applying a well-established methodology and performance metrics. Design/methodology/approach Four feature-based SLAM architectures are evaluated with respect to their suitability for localization of multi-legged walking robots. The evaluation methodology is based on the computation of the absolute trajectory error (ATE) and relative pose error (RPE), which are performance metrics well-established in the robotics community. Four sequences of RGB-D frames acquired in two independent experiments using two different six-legged walking robots are used in the evaluation process. Findings The experiments revealed that the predominant problem characteristics of the legged robots as platforms for SLAM are the abrupt and unpredictable sensor motions, as well as oscillations and vibrations, which corrupt the images captured in-motion. The tested adaptive gait allowed the evaluated SLAM systems to reconstruct proper trajectories. The bundle adjustment-based SLAM systems produced best results, thanks to the use of a map, which enables to establish a large number of constraints for the estimated trajectory. Research limitations/implications The evaluation was performed using indoor mockups of terrain. Experiments in more natural and challenging environments are envisioned as part of future research. Practical implications The lack of accurate self-localization methods is considered as one of the most important limitations of walking robots. Thus, the evaluation of the state-of-the-art SLAM methods on legged platforms may be useful for all researchers working on walking robots’ autonomy and their use in various applications, such as search, security, agriculture and mining. Originality/value The main contribution lies in the integration of the state-of-the-art SLAM methods on walking robots and their thorough experimental evaluation using a well-established methodology. Moreover, a SLAM system designed especially for RGB-D sensors and real-world applications is presented in details.

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Section: Rgb-d Slam V2mentioning

confidence: 99%