Simultaneous Localisation and Mapping (SLAM) is one of the fundamental problems in autonomous mobile robots where a robot needs to reconstruct a previously unseen environment while simultaneously localising itself with respect to the map. In particular, Visual-SLAM uses various sensors from the mobile robot for collecting and sensing a representation of the map. Traditionally, geometric model-based techniques were used to tackle the SLAM problem, which tends to be error-prone under challenging environments. Recent advancements in computer vision, such as deep learning techniques, have provided a data-driven approach to tackle the Visual-SLAM problem. This review summarises recent advancements in the Visual-SLAM domain using various learning-based methods. We begin by providing a concise overview of the geometric model-based approaches, followed by technical reviews on the current paradigms in SLAM. Then, we present the various learning-based approaches to collecting sensory inputs from mobile robots and performing scene understanding. The current paradigms in deep-learning-based semantic understanding are discussed and placed under the context of Visual-SLAM. Finally, we discuss challenges and further opportunities in the direction of learning-based approaches in Visual-SLAM.

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A Review on Visual-SLAM: Advancements from Geometric Modelling to Learning-Based Semantic Scene Understanding Using Multi-Modal Sensor Fusion

Multi-Beam LiDAR (MBL) sensors sample the real-world with discrete 3D point clouds (PC) and have become a major and essential 3D sensing capability for autonomous robots. To ensure an accurate point sampling on surfaces, high-resolution MBL sensors (e.g., Ouster OS0-128) are commonly used to collect dense point clouds for robot tasks, including object detection and tracking, simultaneous localization and mapping (SLAM), in applications such as autonomous driving vehicles (ADVs). However, the high cost and large volume/weight/energy consumption of such sensors limit their usage in broader applications such as UAV/UGV swarms with small-scale agents with limited payload. Existing studies on Super-Resolution (SR) upsampling of the PC from low-resolution MBL have not considered the geometry semantics of the scenes, thus resulting in less optimal SR points for downstream subtasks (e.g., SLAM). Thus, this paper proposes SGSR-Net, a structure semantics-guided MBL Super-Resolution network. SGSR-Net takes the low-resolution range images of the MBL sensors as input and produces dense and structure-aware Super-Resolution point cloud from those sparse measurements through a vertical spatial and channel attention-enhanced CNN model coupling with guided Monte Carlo filtering, for indoor LiDAR-SLAM applications. The SGSR-Net is validated using datasets collected by a UGV equipped with multiple MBL sensors. The results demonstrate that the proposed CG-LSR (CASE Attention Guided Encoder-Decoder LiDAR Super-Resolution Network) reduces the MAE of the SR points by 12.4% down to 0.177 m when compared with the state-of-the-art (SOTA) method [1}, [2], [3], [4]. The indoor SLAM results with SR-points produced by SGSR-Net show that the mean and RMSE of the absolute pose error (APE) are decreased by 27% and 30%, down to 0.849 m and 0.902 m, respectively, which significantly improve the indoor-SLAM performance and stability of SOTA LiDAR-SLAM systems (i.e. LeGO-LOAM [5], [6], [7], [8]). 

SGSR-Net: Structure Semantics Guided LiDAR Super-Resolution Network for Indoor LiDAR SLAM

Simultaneous Localization and Mapping (SLAM) is a computational problem in autonomous robotics that has been a domain of extensive research in the past decade. The significance of the algorithm in ensuring the mainstream application of autonomous robot navigation explains the importance placed on it. Aerial vehicles are employed in several critical industries like defence and forestry to provide accurate surveillance results with the help of onboard navigation and visioning systems. While SLAM algorithms are existent for UAV applications, the application of parallelism on 3D aerial SLAM has not been explored. This paper proposes a parallelized LiDAR 3D SLAM algorithm for aerial autonomous robots. Fast Point Feature Histogram (FPFH) descriptors are extracted for the alignment process which is later finetuned by Iterative Closest Point (ICP) registration. The final estimated trajectory is put through 3D pose graph optimization to minimize the overall drift that may be present The final trajectory and point cloud map is displayed by the algorithm and compared with the ground truth data. The number of threads and CPUs utilized for parallelization of the algorithm have been varied and compared. The simulated results show a significant change of 26% in execution time for the parallelized algorithm utilizing 4 CPUs compared to its serial version.

Parallel FPFH SLAM for Aerial Vehicles

Comision Nacional de Investigacion Cientifica y Tecnologica (CONICYT)

CONICYT FONDECYT

3190147
11180107
ANID PFCHA/Beca de Doctorado Nacional/2019 	
21190489

3D Multiple Sound Source Localization by Proposed Cuboids Nested Microphone Array in Combination with Adaptive Wavelet-Based Subband GEVD

The majority of existing LiDAR odometry solutions are based on simple geometric features such as points, lines or planes which cannot fully reflect the characteristics of surrounding environments. In this study, we propose a novel LiDAR odometry which effectively utilizes the overall exterior characteristics of environmental landmarks. The vehicle pose estimation is accomplished by means of two sequential pose estimation stages, namely, horizontal pose estimation and vertical pose estimation. To achieve effective landmark registration, a comprehensive index is proposed to evaluate the level of similarity between landmarks. This index takes into account two crucial aspects of landmarks, namely, dimension and shape in evaluating their similarity. To assess the performance of the proposed algorithm, we utilize the widely recognized KITTI dataset as well as experimental data collected by an unmanned ground vehicle platform. Both graphical and numerical results indicate that our algorithm outperforms leading LiDAR odometry solutions in terms of positioning accuracy.

L-LO: Enhancing Pose Estimation Precision via a Landmark-Based LiDAR Odometry

Be applied in the large outdoor scenarios, we keep the high-accuracy of robot's mapping and location. Relied on lightweight hardware, we have improved an algorithm of realtime LIDAR SLAM (simultaneous localization and mapping). Compared to vision sensors, LIDARs have higher accuracy and larger range for outdoor scenarios. In the situation of robot moving quickly, LIDARs can keep the features no missing in the scan range, meanwhile camera maybe happen feature fuzzy or missing. Velodyne VLP-16 LIDAR can provide 3D point cloud with 16 channels' scan. By segmenting for LIDAR point clouds, and extracting more special edge or corner feature, we compute robot' motion model that relied on the methods like Point-to-line Iterative Closest Points (PL-ICP) or Point-to-Plane Iterative Closest Points (PP-ICP). Based on robots' motion model, matching point cloud features in a certain range to fix the rigid body motion model. In every certain distant, optimized robot's state information and map based on the method of graph optimization. We use this method to achieve lightweight and high-accuracy LIDAR SLAM for large outdoor, and computed the state of robot. By making experiments in our school, we tested our method for building high accuracy map and providing robot's state.

Low-cost and High-accuracy LIDAR SLAM for Large Outdoor Scenarios

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Low-cost and High-accuracy LIDAR SLAM for Large Outdoor Scenarios