4 research outputs found
Acoustic SLAM based on the Direction-of-Arrival and the Direct-to-Reverberant Energy Ratio
This paper proposes a new method that fuses acoustic measurements in the
reverberation field and low-accuracy inertial measurement unit (IMU) motion
reports for simultaneous localization and mapping (SLAM). Different from
existing studies that only use acoustic data for direction-of-arrival (DoA)
estimates, the source's distance from sensors is calculated with the
direct-to-reverberant energy ratio (DRR) and applied as a new constraint to
eliminate the nonlinear noise from motion reports. A particle filter is applied
to estimate the critical distance, which is key for associating the source's
distance with the DRR. A keyframe method is used to eliminate the deviation of
the source position estimation toward the robot. The proposed DoA-DRR acoustic
SLAM (D-D SLAM) is designed for three-dimensional motion and is suitable for
most robots. The method is the first acoustic SLAM algorithm that has been
validated on a real-world indoor scene dataset that contains only acoustic data
and IMU measurements. Compared with previous methods, D-D SLAM has acceptable
performance in locating the robot and building a source map from a real-world
indoor dataset. The average location accuracy is 0.48 m, while the source
position error converges to less than 0.25 m within 2.8 s. These results prove
the effectiveness of D-D SLAM in real-world indoor scenes, which may be
especially useful in search and rescue missions after disasters where the
environment is foggy, i.e., unsuitable for light or laser irradiation
Optimized self-localization for SLAM in dynamic scenes using probability hypothesis density filters
In many applications, sensors that map the positions of objects in unknown environments are installed on dynamic platforms. As measurements are relative to the observer's sensors, scene mapping requires accurate knowledge of the observer state. However, in practice, observer reports are subject to positioning errors. Simultaneous localization and mapping addresses the joint estimation problem of observer localization and scene mapping. State-of-the-art approaches typically use visual or optical sensors and therefore rely on static beacons in the environment to anchor the observer estimate. However, many applications involving sensors that are not conventionally used for Simultaneous Localization and Mapping (SLAM) are affected by highly dynamic scenes, such that the static world assumption is invalid. This paper proposes a novel approach for dynamic scenes, called GEneralized Motion (GEM) SLAM. Based on probability hypothesis density filters, the proposed approach probabilistically anchors the observer state by fusing observer information inferred from the scene with reports of the observer motion. This paper derives the general, theoretical framework for GEM-SLAM, and shows that it generalizes existing Probability Hypothesis Density (PHD)-based SLAM algorithms. Simulations for a model-specific realization using range-bearing sensors and multiple moving objects highlight that GEM-SLAM achieves significant improvements over three benchmark algorithms