Abstract
Abstract. We present the design for a new indoor mapping system based on three 2D laser scanners as well as a method to process the range measurements such that the pose of the system and the planes of floor, ceiling and walls can be estimated simultaneously. By the combined use of the measurements of all three scanners the pose of the system can be reconstructed in 3D without the need for an IMU. The six pose parameters are modelled as a continuous function over time such that scan line deformations caused by rapid scanner movements do not lead to biases in the estimated poses. The theoretical feasibility of the approach is demonstrated by analysing reconstruction results derived from simulated sensor data of two indoor models. Assuming a perfectly calibrated sensor and ranging noise of 3 cm, the results on data in 10x20 m corridor show that the plane orientation precision is better than 0.1 degree and that the standard deviation of plane-to-plane distances is below 1.5 cm after three loops in the corridor.
Highlights
Indoor position and mapping has been an active research topic within the robotics community for several decades (Thrun, 2002)
While the early research was often motivated by the need for autonomous navigation of robots, including obstacle avoidance, more recent work clearly aims at 3D modelling of the indoor environment (Biber et al, 2004, Borrmann et al, 2008; Henry et al, 2014)
In this paper we have presented the design of a new indoor mapping system based on only three 2D laser scanners
Summary
Indoor position and mapping has been an active research topic within the robotics community for several decades (Thrun, 2002). While Trimble integrated an IMU for indoor positioning of the TIMMS platform (Trimble, 2014), Viametris uses the scans of a horizontally mounted 2D laser scanner in their i-MMS for simultaneous localisation and mapping (SLAM) (Viametris, 2014). In this paper we present the design and data processing for a new indoor mapping system that allows accurate 3D indoor positioning without an IMU. While the hardware is the same as in the i-MMS, we use a different configuration of the three scanners We demonstrate that this enables a new SLAM procedure in which range observations of all three scanners contribute to an accurate 6-DoF pose estimation of the system. The six transformation parameters are described as a function over time using splines In this way the transformation varies from point to point such that scan deformations caused by rapid sensor movements are accurately modelled.
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