Robotic Mapping System Research Articles

Robot-Based Uniform-Coverage and High-Resolution LIDAR Mapping for Physically-Grounded Metaverse Applications

Connecting the tangible world bidirectionally with its digital counterpart is fundamental for Physically-grounded Metaverse Applications (PMAs). For instance, in visually-guided human assistance, space-time analytics, eXtended Reality (XR) or automation use cases, the ability to capture, model, and exploit dynamic environments is essential. PMAs unify tangible and digital premises through intelligent capabilities built on detailed, gap-free and up-to-date 3D models of buildings delivering dependable functionality with utility value. To create and democratize such Metaverse benefits, it is fundamental to enable non-experts to easily capture, annotate and exploit full-fledged 3D models of buildings. Therefore, open IoT-XR platforms, algorithms, tools, and compelling user experiences are needed. Key factors to address such production-level 3D mapping technologies are time, cost, instrumentation- and operational-complexity for generating high-resolution maps with uniform-coverage. This work introduces an 3D surveying method via a semi-automatic mobile robot-mapping system. The robotic platform and novel algorithms improve the mapping process by ensuring sampling regularity, increasing spatial coverage, rejecting outlier points, and reducing total mapping time. This article focuses on real-world challenges and lessons learned from a novel closed-loop volumetric scanning-and-analysis method. Extensive experiments support claims in large maps.

GPU-Accelerated Incremental Euclidean Distance Transform for Online Motion Planning of Mobile Robots

In this letter, we present a volumetric mapping system that effectively calculates Occupancy Grid Maps (OGMs) and Euclidean Distance Transforms (EDTs) with parallel computing. Unlike these mappers for high-precision structural reconstruction, our system incrementally constructs global EDT and outputs high-frequency local distance information for online robot motion planning. The proposed system receives multiple types of sensor inputs and constructs OGM without down-sampling. Using GPU programming techniques, the system quickly computes EDT in parallel within local volume. The new observation is continuously integrated into the global EDT using the parallel wavefront algorithm while preserving the historical observations. Experiments with datasets have shown that our proposed approach outperforms existing state-of-the-art robot mapping systems and is particularly suitable for mapping unexplored areas. In its actual implementations on aerial and ground vehicles, the proposed system achieves real-time performance with limited onboard computational resources.

Robotic Mapping Approach under Illumination-Variant Environments at Planetary Construction Sites

In planetary construction, the semiautonomous teleoperation of robots is expected to perform complex tasks for site preparation and infrastructure emplacement. A highly detailed 3D map is essential for construction planning and management. However, the planetary surface imposes mapping restrictions due to rugged and homogeneous terrains. Additionally, changes in illumination conditions cause the mapping result (or 3D point-cloud map) to have inconsistent color properties that hamper the understanding of the topographic properties of a worksite. Therefore, this paper proposes a robotic construction mapping approach robust to illumination-variant environments. The proposed approach leverages a deep learning-based low-light image enhancement (LLIE) method to improve the mapping capabilities of the visual simultaneous localization and mapping (SLAM)-based robotic mapping method. In the experiment, the robotic mapping system in the emulated planetary worksite collected terrain images during the daytime from noon to late afternoon. Two sets of point-cloud maps, which were created from original and enhanced terrain images, were examined for comparison purposes. The experiment results showed that the LLIE method in the robotic mapping method significantly enhanced the brightness, preserving the inherent colors of the original terrain images. The visibility and the overall accuracy of the point-cloud map were consequently increased.

Bio-inspired homogeneous multi-scale place recognition

Robotic mapping and localization systems typically operate at either one fixed spatial scale, or over two, combining a local metric map and a global topological map. In contrast, recent high profile discoveries in neuroscience have indicated that animals such as rodents navigate the world using multiple parallel maps, with each map encoding the world at a specific spatial scale. While a number of theoretical-only investigations have hypothesized several possible benefits of such a multi-scale mapping system, no one has comprehensively investigated the potential mapping and place recognition performance benefits for navigating robots in large real world environments, especially using more than two homogeneous map scales. In this paper we present a biologically-inspired multi-scale mapping system mimicking the rodent multi-scale map. Unlike hybrid metric-topological multi-scale robot mapping systems, this new system is homogeneous, distinguishable only by scale, like rodent neural maps. We present methods for training each network to learn and recognize places at a specific spatial scale, and techniques for combining the output from each of these parallel networks. This approach differs from traditional probabilistic robotic methods, where place recognition spatial specificity is passively driven by models of sensor uncertainty. Instead we intentionally create parallel learning systems that learn associations between sensory input and the environment at different spatial scales. We also conduct a systematic series of experiments and parameter studies that determine the effect on performance of using different neural map scaling ratios and different numbers of discrete map scales. The results demonstrate that a multi-scale approach universally improves place recognition performance and is capable of producing better than state of the art performance compared to existing robotic navigation algorithms. We analyze the results and discuss the implications with respect to several recent discoveries and theories regarding how multi-scale neural maps are learnt and used in the mammalian brain.

A hierarchical model of goal directed navigation selects trajectories in a visual environment

We have developed a Hierarchical Look-Ahead Trajectory Model (HiLAM) that incorporates the firing pattern of medial entorhinal grid cells in a planning circuit that includes interactions with hippocampus and prefrontal cortex. We show the model’s flexibility in representing large real world environments using odometry information obtained from challenging video sequences. We acquire the visual data from a camera mounted on a small tele-operated vehicle. The camera has a panoramic field of view with its focal point approximately 5cm above the ground level, similar to what would be expected from a rat’s point of view. Using established algorithms for calculating perceptual speed from the apparent rate of visual change over time, we generate raw dead reckoning information which loses spatial fidelity over time due to error accumulation. We rectify the loss of fidelity by exploiting the loop-closure detection ability of a biologically inspired, robot navigation model termed RatSLAM. The rectified motion information serves as a velocity input to the HiLAM to encode the environment in the form of grid cell and place cell maps. Finally, we show goal directed path planning results of HiLAM in two different environments, an indoor square maze used in rodent experiments and an outdoor arena more than two orders of magnitude larger than the indoor maze. Together these results bridge for the first time the gap between higher fidelity bio-inspired navigation models (HiLAM) and more abstracted but highly functional bio-inspired robotic mapping systems (RatSLAM), and move from simulated environments into real-world studies in rodent-sized arenas and beyond.

Mapping utility infrastructure via underground GPS positioning with autonomous telerobotics

This paper presents technology applications from the autonomous mining and construction industries in tunnel and underground environments as applied to critical large diameter utility infrastructure. A self-contained inertial navigation system for the positioning and mapping of underground infrastructures is a significant development in tunnel profiling, 3D referencing and gyro/laser surveying; a key service offered by the results of this project. The underground positioning relies on a network of satellites placed to surround an area of interest, with a range of up to 2km through soil or rock with accuracy better than 3%, enabling accurate positioning of underground assets. The robotic mapping system has the capabilities to accurately map tunnels, pipes and conduits, in detail and sequentially transfer the data collected into popular engineering CAD systems. A specialized military grade inertial referencing system (IRS) linked to multiple scanners provides high precision profiling while measuring roughness, deflection, ovality and positioning. The IRS component is linked to multiple laser scanners supplying high precision profiling while being driven forward. Laser scanning collects hundreds of data points per second linked to an accurate position through the IRS. All data is collected to on-board computer hard drives and transferred to the engineering office via memory storage systems or directly by wireless networks set up within the pipeline. Combining sectional scans with positioning and altitude data in real time creates 3D maps for surface referencing, a valuable service for pinpointing underground infrastructure problem locations in relation to surface features enabling informed risk management decisions.

A Robotic Indoor 3D Mapping System Using a 2D Laser Range Finder Mounted on a Rotating Four-Bar Linkage of a Mobile Platform

This paper describes our work in developing a 3D robotic mapping system composed by an experimental mobile platform equipped with a rotating laser range finder (LRF). For the purpose of obtaining more complete 3D scans of the environment, we design, construct and calibrate a crank-rocker four-bar linkage so that a LRF mounted on it could undergo repetitive rotational motion between two extreme positions, allowing both horizontal and vertical scans. To reduce the complexity of map representation suitable for optimization later, the local map from the LRF is a grid map represented by a distance-transformed (DT) matrix. We compare the DT-transformed maps and find the transformation matrix of a robot pose by a linear simplex-based map optimization method restricted to a local region allows efficient alignment of maps in scan matching. Several indoor 2D and 3D mapping experiments are presented to demonstrate the consistency, efficiency and accuracy of the 3D mapping system for a mobile robot that is stationary or in motion.