LiDAR Localization and Mapping for Autonomous Vehicles: Recent Solutions and Trends

Skrzypczyński, Piotr

doi:10.1007/978-3-030-74893-7_24

Cited by 1 publication

(1 citation statement)

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“…Therefore, simultaneous localization and mapping [1] and visual odometry (VO) [2] are considered a complementary means of yielding vehicle pose estimates that enable safe navigation of autonomous or semi-autonomous vehicles. While there is a large body of research on SLAM and VO, there are still many open issues when it comes to more complicated yet more practical cases, including 3D perception and lifelong map learning [3].…”

Section: Introductionmentioning

confidence: 99%

Practical Insights on Automotive SLAM in Urban Environments

Skrzypczyński¹

2023

Autonomous Mobile Mapping Robots

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This chapter tackles the issues of simultaneous localization and mapping (SLAM) using laser scanners or vision as a viable alternative to the accurate modes of satellite-based localization, which are popular and easy to implement with modern technology but might fail in many urban scenarios. This chapter considers two state-of-the-art localization algorithms, LOAM and ORB-SLAM3 that use the optimization-based formulation of SLAM and utilize laser and vision sensing, respectively. The focus is on the practical aspects of localization and the accuracy of the obtained trajectories. It contributes to a series of experiments conducted using an electric car equipped with a carefully calibrated multisensory setup with a 3D laser scanner, camera, and a smartphone for collecting the exteroceptive measurements. Results of applying the two different SLAM algorithms to the data sequences collected with the vehicle-based multisensory setup clearly demonstrate that not only the expensive laser sensors but also monocular vision, including the commodity smartphone camera, can be used to obtain off-line reasonably accurate vehicle trajectories in an urban environment.

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Section: Introductionmentioning

confidence: 99%