A fully autonomous indoor mobile robot using SLAM

Riaz, Zahid; Pervez, Aslam; Ahmer, M.; Iqbal, Jawad

doi:10.1109/iciet.2010.5625691

Cited by 4 publications

(3 citation statements)

References 9 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The common estimation frameworks to produce a SLAM solution are based on Kalman filter and Particle filter (PF) [8]. PF was implemented in [2] to integrate motion (heading and translation) measurements from a monocular camera, foot‐mounted IMU, sound navigation and ranging (SONAR), and a barometer to produce an accurate and reliable 3D localisation solution for SLAM.…”

Section: Introductionmentioning

confidence: 99%

“…LiDAR is commonly utilised in mobile laser scanning (MLS). The MLS systems also rely on GNSS, mainly for direct georeferencing of point clouds [7], but the increasing number of autonomous robotic platform applications [4,8] has emphasised the need for better location accuracy. The growing demand for robotic and situational awareness applications in GNSS denied environments will emphasise the role of multi-sensor indoor positioning [9].…”

mentioning

confidence: 99%

“…Three-dimensional (3D) detection of features with LiDAR is also common in simultaneous localisation and mapping (SLAM) [6]. SLAM provides infrastructure-free accurate and reliable localisation and information on the environment by means of adaptive integration of data from multiple sensors [8,10]. In SLAM, LiDAR is typically combined with other optical and odometry sensors, IMU [4], and GNSS in outdoor applications [11].…”

mentioning

confidence: 99%

See 2 more Smart Citations

Towards multispectral, multi‐sensor indoor positioning and target identification

Kaasalainen¹,

Ruotsalainen²,

Kirkko-Jaakkola³

et al. 2017

Electron. lett.

View full text Add to dashboard Cite

A concept and first results of combining multispectral light detection and ranging (LiDAR) with positioning sensors to produce spatially resolved target identification in indoor environment is presented. The aim is to enhance the sensor-based indoor localisation with a multispectral target identification and mapping. There is a growing need for automatic and mobile mapping and surveillance in buildings and locations where satellite positioning is not available. LiDAR is a common sensor in feature-based simultaneous localisation and mapping. As multispectral LiDARs are emerging and becoming increasingly popular in research applications, the multi/hyperspectral point clouds are likely to improve object recognition and enable a new level of autonomous surveillance in the near future. The first results show that position solution can be obtained using sensors attached to the LiDAR.

show abstract

Section: Introductionmentioning

confidence: 99%

mentioning

confidence: 99%