This article presents a survey of simultaneous localization and mapping (SLAM) and data fusion techniques for object detection and environmental scene perception in unmanned aerial vehicles (UAVs). We critically evaluate some current SLAM implementations in robotics and autonomous vehicles and their applicability and scalability to UAVs. SLAM is envisioned as a potential technique for object detection and scene perception to enable UAV navigation through continuous state estimation. In this article, we bridge the gap between SLAM and data fusion in UAVs while also comprehensively surveying related object detection techniques such as visual odometry and aerial photogrammetry. We begin with an introduction to applications where UAV localization is necessary, followed by an analysis of multimodal sensor data fusion to fuse the information gathered from different sensors mounted on UAVs. We then discuss SLAM techniques such as Kalman filters and extended Kalman filters to address scene perception, mapping, and localization in UAVs. The findings are summarized to correlate prevalent and futuristic SLAM and data fusion for UAV navigation, and some avenues for further research are discussed.

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Simultaneous Localization and Mapping (SLAM) and Data Fusion in Unmanned Aerial Vehicles: Recent Advances and Challenges

Discriminative feature learning using a multiscale convolutional capsule network from attitude data for fault diagnosis of industrial robots

This article presents a comprehensive review of the Active Simultaneous Localization and Mapping (A-SLAM) research conducted over the past decade. It explores the formulation, applications, and methodologies employed in A-SLAM, particularly in trajectory generation and control-action selection, drawing on concepts from Information Theory (IT) and the Theory of Optimal Experimental Design (TOED). This review includes both qualitative and quantitative analyses of various approaches, deployment scenarios, configurations, path-planning methods, and utility functions within A-SLAM research. Furthermore, this article introduces a novel analysis of Active Collaborative SLAM (AC-SLAM), focusing on collaborative aspects within SLAM systems. It includes a thorough examination of collaborative parameters and approaches, supported by both qualitative and statistical assessments. This study also identifies limitations in the existing literature and suggests potential avenues for future research. This survey serves as a valuable resource for researchers seeking insights into A-SLAM methods and techniques, offering a current overview of A-SLAM formulation. 

Active SLAM: A Review on Last Decade

We demonstrate an optical-camera-communication (OCC) system utilizing a laser-diode (LD) coupled optical-diffusing-fiber (ODF) transmitter (Tx) and rolling-shutter based image sensor receiver (Rx). The ODF is a glass optical fiber produced for decorative lighting or embedded into small areas where bulky optical sources cannot fit. Besides, decoding the high data rate rolling-shutter pattern from the thin ODF Tx is very challenging. Here, we propose and experimentally demonstrate the pixel-row-per-bit based neural-network (PPB-NN) to decode the rolling-shutter-pattern emitted by the thin ODF Tx. The proposed PPB-NN algorithm is discussed. The proposed PPB-NN method can satisfy the pre-forward error correction (FEC) BER at data rate of 3,300 bit/s at a transmission distance of 35 cm. Theoretical analysis of the maximum ODF Tx angle is also discussed; and our experimental values agree with our theoretical results. 

Large field-of-view (FOV) optical camera communication (OCC) using optical-diffusing fiber (ODF) and rolling shutter image sensor

Visible light positioning (VLP) is a promising technology since it can provide high-accuracy indoor localization based on the existing lighting infrastructure. Most VLP systems require a prior light-emitting diode (LED) location map, termed a VLP-landmark map in this article, for which manual surveys are costly in practical deployment at scale. What is more, the existing approaches also require dense LED deployments. In this work, we proposed a multisensor fusion framework, termed VWR-simultaneous localization and mapping (SLAM), which tightly fused the VLP, wheel odometer, and red green dlue-depth map (RGB-D) camera to achieve SLAM. Our VWR-SLAM can provide accurate and robust robot localization and navigation in LED shortage/outage situations, meanwhile, constructing the 3-D sparse environment map and the 3-D VLP-landmark map without tedious manual measurements. The experimental results show that our proposed scheme can provide an average robot positioning accuracy of 1.81 cm and an LED mapping accuracy of 3.01 cm. 

VWR-SLAM: Tightly Coupled SLAM System Based on Visible Light Positioning Landmark, Wheel Odometer, and RGB-D Camera

Visible light positioning (VLP) is a promising technology since it can provide high accuracy indoor localization based on the existing lighting infrastructure. However, existing approaches often require dense LED distributions and persistent line-of-sight (LOS) between transmitter and receiver. What's more, sensors are imperfect, and their measurements are prone to errors. Through multi sensors fusion, we can compensate the deficiencies of stand-alone sensors and provide more reliable pose estimations. In this work, we propose a loosely-coupled multi-sensor fusion method based on VLP and Simultaneous Localization and Mapping (SLAM), using light detection and ranging (LiDAR), odometry, and rolling shutter camera. Our multi-sensor localizer can provide accurate and robust robot localization and navigation in LED shortage/outage situations. The experimental results show that our proposed scheme can provide an average accuracy of 2.5 cm with around 42 ms average positioning latency.

/pdf/robot-localization-and-navigation-using-visible-light-1vcisqqpqr.pdf

Robot Localization and Navigation Using Visible Light Positioning and SLAM Fusion

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Robot Localization and Navigation Using Visible Light Positioning and SLAM Fusion