Abstract
Many applications require the localization of a moving object, e.g., a robot, using sensory data acquired from embedded devices. Simultaneous localization and mapping from vision performs both the spatial and temporal fusion of these data on a map when a camera moves in an unknown environment. Such a SLAM process executes two interleaved functions: the front-end detects and tracks features from images, while the back-end interprets features as landmark observations and estimates both the landmarks and the robot positions with respect to a selected reference frame. This paper describes a complete visual SLAM solution, combining both point and line landmarks on a single map. The proposed method has an impact on both the back-end and the front-end. The contributions comprehend the use of heterogeneous landmark-based EKF-SLAM (the management of a map composed of both point and line landmarks); from this perspective, the comparison between landmark parametrizations and the evaluation of how the heterogeneity improves the accuracy on the camera localization, the development of a front-end active-search process for linear landmarks integrated into SLAM and the experimentation methodology.
Highlights
Simultaneous localization and mapping (SLAM) is an essential functionality required on a moving object for many applications where the localization or the motion estimation of this object must be determined from sensory data acquired by embedded sensors
Even for the anchored cases, already having a relative good performance while working independently, the heterogeneity improves the results, in such a way that the combination of both Anchored homogeneous point (AHP) and Anchored homogeneous-points line (AHPL) is the one with the least error along the simulated trajectories
The purpose of this paper is to prove the benefits of including heterogeneous landmarks when building a map from an EKF-based visual SLAM method
Summary
Simultaneous localization and mapping (SLAM) is an essential functionality required on a moving object for many applications where the localization or the motion estimation of this object must be determined from sensory data acquired by embedded sensors. The object is typically a robot or a vehicle, the position of which is required to deal with robust navigation in a cluttered environment. The robot or smart tool could be equipped with a global navigation satellite system (GNSS). The direct localization is not always available (i.e., occlusions, bad propagation, multiple paths); so generally, they are combined using loose or tie fusion strategies, with motion estimates provided by an inertial measurement unit (IMU), integrating successive accelerometer and gyro data [8,9,10]. A priori knowledge could be exploited; Sensors 2016, 16, 489; doi:10.3390/s16040489 www.mdpi.com/journal/sensors
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