Construction and Calibration of a Low-Cost 3D Laser Scanner with 360◦ Field of View for Mobile Robots

Navigation of many mobile robots relies on environmental information obtained from three-dimensional (3D) laser scanners. This paper presents a new 360◦ field-of-view 3D laser scanner for mobile robots that avoids the high cost of commercial devices. The 3D scanner is based on spinning a Hokuyo UTM- 30LX-EX two-dimensional (2D) rangefinder around its optical center. The proposed design profits from lessons learned with the development of a previous 3D scanner with pitching motion. Intrinsic calibration of the new device has been performed to obtain both temporal and geometric parameters. The paper also shows the integration of the 3D device in the outdoor mobile robot Andabata.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tec

An Architecture for Online Affordance-based Perception and Whole-body Planning

The DARPA Robotics Challenge Trials held in December 2013 provided a landmark demonstration of dexterous mobile robots executing a variety of tasks aided by a remote human operator using only data from the robot's sensor suite transmitted over a constrained, field-realistic communications link. We describe the design considerations, architecture, implementation and performance of the software that Team MIT developed to command and control an Atlas humanoid robot. Our design emphasized human interaction with an efficient motion planner, where operators expressed desired robot actions in terms of affordances fit using perception and manipulated in a custom user interface. We highlight several important lessons we learned while developing our system on a highly compressed schedule

3D Perception Based Lifelong Navigation of Service Robots in Dynamic Environments

Lifelong navigation of mobile robots is to ability to reliably operate over extended periods of time in dynamically changing environments. Historically, computational capacity and sensor capability have been the constraining factors to the richness of the internal representation of the environment that a mobile robot could use for navigation tasks. With affordable contemporary sensing technology available that provides rich 3D information of the environment and increased computational power, we can increasingly make use of more semantic environmental information in navigation related tasks.A navigation system has many subsystems that must operate in real time competing for computation resources in such as the perception, localization, and path planning systems. The main thesis proposed in this work is that we can utilize 3D information from the environment in our systems to increase navigational robustness without making trade-offs in any of the real time subsystems. To support these claims, this dissertation presents robust, real world 3D perception based navigation systems in the domains of indoor doorway detection and traversal, sidewalk-level outdoor navigation in urban environments, and global localization in large scale indoor warehouse environments.The discussion of these systems includes methods of 3D point cloud based object detection to find respective objects of semantic interest for the given navigation tasks as well as the use of 3D information in the navigational systems for purposes such as localization and dynamic obstacle avoidance. Experimental results for each of these applications demonstrate the effectiveness of the techniques for robust long term autonomous operation

Enabling Multi-LiDAR Sensing in GNSS-Denied Environments: SLAM Dataset, Benchmark, and UAV Tracking with LiDAR-as-a-camera

The rise of Light Detection and Ranging (LiDAR) sensors has profoundly impacted industries ranging from automotive to urban planning. As these sensors become increasingly affordable and compact, their applications are diversifying, driving precision, and innovation. This thesis delves into LiDAR's advancements in autonomous robotic systems, with a focus on its role in simultaneous localization and mapping (SLAM) methodologies and LiDAR as a camera-based tracking for Unmanned Aerial Vehicles (UAV). Our contributions span two primary domains: the Multi-Modal LiDAR SLAM Benchmark, and the LiDAR-as-a-camera UAV Tracking. In the former, we have expanded our previous multi-modal LiDAR dataset by adding more data sequences from various scenarios. In contrast to the previous dataset, we employ different ground truth-generating approaches. We propose a new multi-modal multi-lidar SLAM-assisted and ICP-based sensor fusion method for generating ground truth maps. Additionally, we also supplement our data with new open road sequences with GNSS-RTK. This enriched dataset, supported by high-resolution LiDAR, provides detailed insights through an evaluation of ten configurations, pairing diverse LiDAR sensors with state-of-the-art SLAM algorithms. In the latter contribution, we leverage a custom YOLOv5 model trained on panoramic low-resolution images from LiDAR reflectivity (LiDAR-as-a-camera) to detect UAVs, demonstrating the superiority of this approach over point cloud or image-only methods. Additionally, we evaluated the real-time performance of our approach on the Nvidia Jetson Nano, a popular mobile computing platform. Overall, our research underscores the transformative potential of integrating advanced LiDAR sensors with autonomous robotics. By bridging the gaps between different technological approaches, we pave the way for more versatile and efficient applications in the future

Automatic Romaine Heart Harvester

The Romaine Robotics Senior Design Team developed a romaine lettuce heart trimming system in partnership with a Salinas farm to address a growing labor shortage in the agricultural industry that is resulting in crops rotting in the field before they could be harvested. An automated trimmer can alleviate the most time consuming step in the cut-trim-bag harvesting process, increasing the yields of robotic cutters or the speed of existing laborer teams. Leveraging the Partner Farm’s existing trimmer architecture, which consists of a laborer loading lettuce into sprungloaded grippers that are rotated through vision and cutting systems by an indexer, the team redesigned geometry to improve the loading, gripping, and ejection stages of the system. Physical testing, hand calculations, and FEA were performed to understand acceptable grip strengths and cup design, and several wooden mockups were built to explore a new actuating linkage design for the indexer. The team manufactured, assembled, and performed verification testing on a full-size metal motorized prototype that can be incorporated with the Partner Farm’s existing cutting and vision systems. The prototype met all of the established requirements, and the farm has implemented the redesign onto their trimmer. Future work would include designing and implementing vision and cutting systems for the team’s metal prototype

Calibration of Multi-LIDAR Systems for Real-Time Surface Management: Application in Bucket Wheel Reclaimer

Stockpile reclaiming using a Bucket Wheel Reclaimer (BWR) is an important part of stockyard management. The growth in demand for material handling over the years has drawn attention to improve the automation of the process. However, studies have shown that stockpiled products are being reclaimed at approximately 50% of their potential. This study focuses on the challenges in the automation of stockyard management system using a BWR.For high accuracy point cloud computation and surface reconstruction of the stockpiled materials, accurate calibration is of crucial importance. This dissertation presents a calibration technique for multi-LIDAR systems to estimate the GNSS-LIDAR extrinsic parameters of BWR's. The approach presented works with one or more 2D LIDARs and does not require special markers (e.g., reflective tape) or surveyed locations (other than a DGNSS base station antenna). The method and its accuracy have been demonstrated using experimental data from a stockyard environment.Regarding real-time management and control, the dissertation presents a technique for real-time point cloud management, visualization, and feature extraction for large scale stockyards environments. The software solution described continuously manages the point cloud in real-time as the sensors stream data. It also displays the current stockpile on an interactive interface that allows the user to see the surface from different view-points, interrogate the coordinates of any surface location, and computes the BWR entry and exit point for automated operation. The software is tested using experimental data from a port located in Yantai, China

PERISCOPE: PERIapsis Subsurface Cave Optical Explorer

The PERISCOPE study focuses primarily on lunar caves, due to the potential for being imaged in orbital scenarios. In the intervening years, from 2012-2015, scientists developed further rationales and interest in the scientific value of lunar caves. It does not appear that they are likely to be sinks for water-ice due to the relatively warm temperatures(~-20 degrees Celsius) in the caves leading to geologically-rapid migration of unbound water due to sublimation, and inevitable loss through any skylights. However, the skylights themselves reveal apparent complex layering, which may speak to a more complex multi-stage evolution of mare flood basalts than previously considered, and so their examination may provide even more insight into the lunar mare, which in turn provide a primary record of early solar system crustal formal and evolution processes. Further extrapolation of these insights can be found within the exoplanet community of researchers,who find the information useful for calibrating star formation and planetary evolution models. In addition, catalogues of lunar and martian skylights, "caves" or "atypical pit craters" have been developed, with numbers for both bodies now in the low hundreds thanks to additional high resolution surveys and revisiting the existing image databases