Simultaneous Localization and Mapping (SLAM) for Autonomous Driving: Concept and Analysis

The Simultaneous Localization and Mapping (SLAM) technique has achieved astonishing progress over the last few decades and has generated considerable interest in the autonomous driving community. With its conceptual roots in navigation and mapping, SLAM outperforms some traditional positioning and localization techniques since it can support more reliable and robust localization, planning, and controlling to meet some key criteria for autonomous driving. In this study the authors first give an overview of the different SLAM implementation approaches and then discuss the applications of SLAM for autonomous driving with respect to different driving scenarios, vehicle system components and the characteristics of the SLAM approaches. The authors then discuss some challenging issues and current solutions when applying SLAM for autonomous driving. Some quantitative quality analysis means to evaluate the characteristics and performance of SLAM systems and to monitor the risk in SLAM estimation are reviewed. In addition, this study describes a real-world road test to demonstrate a multi-sensor-based modernized SLAM procedure for autonomous driving. The numerical results show that a high-precision 3D point cloud map can be generated by the SLAM procedure with the integration of Lidar and GNSS/INS. Online four–five cm accuracy localization solution can be achieved based on this pre-generated map and online Lidar scan matching with a tightly fused inertial system

Tightly Coupled 3D Lidar Inertial Odometry and Mapping

Ego-motion estimation is a fundamental requirement for most mobile robotic applications. By sensor fusion, we can compensate the deficiencies of stand-alone sensors and provide more reliable estimations. We introduce a tightly coupled lidar-IMU fusion method in this paper. By jointly minimizing the cost derived from lidar and IMU measurements, the lidar-IMU odometry (LIO) can perform well with acceptable drift after long-term experiment, even in challenging cases where the lidar measurements can be degraded. Besides, to obtain more reliable estimations of the lidar poses, a rotation-constrained refinement algorithm (LIO-mapping) is proposed to further align the lidar poses with the global map. The experiment results demonstrate that the proposed method can estimate the poses of the sensor pair at the IMU update rate with high precision, even under fast motion conditions or with insufficient features.Comment: Accepted by ICRA 201

Microdrone-Based Indoor Mapping with Graph SLAM

Unmanned aerial vehicles offer a safe and fast approach to the production of three-dimensional spatial data on the surrounding space. In this article, we present a low-cost SLAM-based drone for creating exploration maps of building interiors. The focus is on emergency response mapping in inaccessible or potentially dangerous places. For this purpose, we used a quadcopter microdrone equipped with six laser rangefinders (1D scanners) and an optical sensor for mapping and positioning. The employed SLAM is designed to map indoor spaces with planar structures through graph optimization. It performs loop-closure detection and correction to recognize previously visited places, and to correct the accumulated drift over time. The proposed methodology was validated for several indoor environments. We investigated the performance of our drone against a multilayer LiDAR-carrying macrodrone, a vision-aided navigation helmet, and ground truth obtained with a terrestrial laser scanner. The experimental results indicate that our SLAM system is capable of creating quality exploration maps of small indoor spaces, and handling the loop-closure problem. The accumulated drift without loop closure was on average 1.1% (0.35 m) over a 31-m-long acquisition trajectory. Moreover, the comparison results demonstrated that our flying microdrone provided a comparable performance to the multilayer LiDAR-based macrodrone, given the low deviation between the point clouds built by both drones. Approximately 85 % of the cloud-to-cloud distances were less than 10 cm

Multiple Integrated Navigation Sensors for Improving Occupancy Grid FastSLAM

An autonomous vehicle must accurately observe its location within the environment to interact with objects and accomplish its mission. When its environment is unknown, the vehicle must construct a map detailing its surroundings while using it to maintain an accurate location. Such a vehicle is faced with the circularly defined Simultaneous Localization and Mapping (SLAM) problem. However difficult, SLAM is a critical component of autonomous vehicle exploration with applications to search and rescue. To current knowledge, this research presents the first SLAM solution to integrate stereo cameras, inertial measurements, and vehicle odometry into a Multiple Integrated Navigation Sensor (MINS) path. The implementation combines the MINS path with LIDAR to observe and map the environment using the FastSLAM algorithm. In real-world tests, a mobile ground vehicle equipped with these sensors completed a 140 meter loop around indoor hallways. This SLAM solution produces a path that closes the loop and remains within 1 meter of truth, reducing the error 92% from an image-inertial navigation system and 79% from odometry FastSLAM

A Comprehensive Review on Autonomous Navigation

The field of autonomous mobile robots has undergone dramatic advancements over the past decades. Despite achieving important milestones, several challenges are yet to be addressed. Aggregating the achievements of the robotic community as survey papers is vital to keep the track of current state-of-the-art and the challenges that must be tackled in the future. This paper tries to provide a comprehensive review of autonomous mobile robots covering topics such as sensor types, mobile robot platforms, simulation tools, path planning and following, sensor fusion methods, obstacle avoidance, and SLAM. The urge to present a survey paper is twofold. First, autonomous navigation field evolves fast so writing survey papers regularly is crucial to keep the research community well-aware of the current status of this field. Second, deep learning methods have revolutionized many fields including autonomous navigation. Therefore, it is necessary to give an appropriate treatment of the role of deep learning in autonomous navigation as well which is covered in this paper. Future works and research gaps will also be discussed

Invariant EKF Design for Scan Matching-aided Localization

Localization in indoor environments is a technique which estimates the robot's pose by fusing data from onboard motion sensors with readings of the environment, in our case obtained by scan matching point clouds captured by a low-cost Kinect depth camera. We develop both an Invariant Extended Kalman Filter (IEKF)-based and a Multiplicative Extended Kalman Filter (MEKF)-based solution to this problem. The two designs are successfully validated in experiments and demonstrate the advantage of the IEKF design

Laserbasert oppmåling av bygningsobjekter og bygninger

Building information models (BIMs) for facility management is gaining interest. Different technologies for collecting the raw material to extract such model are in rapid development. The most common technologies are based on images, structure light, laser or a combination of these. The new technologies have the potential to provide efficient data collection, but not necessarily at the same accuracy compared to the traditional methods. This thesis has explored how to rapidly establish a BIM for an existing building. This was done by investigating two different aspects related to this task. The first aspect was related to product specification and provide a framework for ordering and controlling a laser-based survey of a building. The second aspect explores how a laser-based system could be used to rapidly survey an existing building. Through the thesis and the first aspect, it is shown that the Norwegian survey community is lacking an unambiguous product specification for building surveys performed for BIM extraction and that the survey seldomly is adequately controlled. Based on these findings a product specification has been developed in cooperation with building owners. This cooperation made it possible to test the product specification in real projects. The product specification was developed through three different versions. The zero version was presented at the World Building Congress in 2016 and was tested in a renovation project at the Norwegian University of Life Sciences. The evaluation of the project led to the first version that was used in a framework competition arranged by Ullensaker municipality in the south-east of Norway. The result led to the second and final version of the product specification. The proposed product specification follows a simplified transaction pattern between the customer and the producer. The focus has been on the customer's request for a building survey suitable for BIM extraction and the customer's acceptance actions when the building survey is delivered. The acceptance actions are based on well–known standards created by the Norwegian Mapping Authority. The customer request is based on the acceptance actions. This ensures that every requirements can be verified in the accepting stage. The main purposes of the product specification were to ensure reliable results and to minimize the difference between the customer request and the producer’s delivery. Additionally, an unambiguous product specification can ensure a fair competition situation between the producers and give the producers the possibility to select the best-suited technology. The second aspect is related to how a building can be efficiently surveyed and explores how this could be done with a laser-based system. A human carried survey system was developed through three stages. The first and second stages focused on circle shaped objects and were realized in cooperation with the Faculty of Environmental Sciences and Natural Resource Management at the Norwegian University of Life Sciences. The system surveyed tree diameter at breast height within sample plots in size 250-500 m2. The system was able to detect 87.5% of the trees with a mean difference of 0.1 cm, and a root mean square of 2.2 cm. The novel aspect is related to how the trees are segmented and how the diameters are estimated without losing precision due to degraded pose solution. The result can be used in forestry inventory projects together with airborne laser surveys. The third stage was made for indoor measurements. The main focus was on how to aid the navigation solution in the absence of Global Navigation Satellite System signals. The method divides the laser point measurements into small time frames. For each time frame, the laser points were automatically classified into column, walls, floor, and ceiling. This information was used to support a scan matching method called semantic-assisted normal distributions transform. The result from the scan matching was used to create a trajectory of the walking path followed during data capture. This result was fed back into the inertial navigation processing to aid the solution when the system was located inside the building. This gives the inertial navigation process the ability to reject scan matching failures. The novel method was able to improve the survey accuracy from a maximum deviation of 12.6 m to 1.1 m. The third stage had two different Inertial Measurement Units (IMU) installed. The most accurate system was a tactical graded IMU, and the lowest accurate system was an automotive graded IMU. With the proposed method, the automotive graded system was able to perform at a higher level than a standalone tactical graded solution.Interessen for å bruke BygningsInformasjonsModeller (BIMer) i forvaltning, drift og vedlikehold av bygninger er økende. Ulike teknologier for innsamling av data for å etablere slike modeller er i rask utvikling. De vanligste teknologiene er basert på bilder, strukturert lys, laser eller en kombinasjon av disse. Ny teknologi utfører målingene veldig effektivt, men ikke med samme nøyaktighet som tradisjoneller metoder. Denne studien har undersøkt hvordan en raskt kan etablere en BIM i et eksisterende bygg. Dette ble gjort ved å utforske to ulike aspekter av problemstillingen. Det første aspektet ser på produktspesifikasjon og foreslår et rammeverk til bruk ved bestilling og kontroll av laser-basert innmåling av eksisterende bygning. Det andre aspektet utforsker hvordan et laser-basert system raskt kan måle opp eksisterende bygg.The Norwegian Building Authority, Cautus Geo AS and Geomatikk survey have kindly founded parts of the studies