Gaussian Process Distance Fields Obstacle and Ground Constraints for Safe Navigation

Navigating cluttered environments is a challenging task for any mobile system. Existing approaches for ground-based mobile systems primarily focus on small wheeled robots, which face minimal constraints with overhanging obstacles and cannot manage steps or stairs, making the problem effectively 2D. However, navigation for legged robots (or even humans) has to consider an extra dimension. This paper proposes a tailored scene representation coupled with an advanced trajectory optimisation algorithm to enable safe navigation. Our 3D navigation approach is suitable for any ground-based mobile robot, whether wheeled or legged, as well as for human assistance. Given a 3D point cloud of the scene and the segmentation of the ground and non-ground points, we formulate two Gaussian Process distance fields to ensure a collision-free path and maintain distance to the ground constraints. Our method adeptly handles uneven terrain, steps, and overhanging objects through an innovative use of a quadtree structure, constructing a multi-resolution map of the free space and its connectivity graph based on a 2D projection of the relevant scene. Evaluations with both synthetic and real-world datasets demonstrate that this approach provides safe and smooth paths, accommodating a wide range of ground-based mobile systems.

Real-Time Truly-Coupled Lidar-Inertial Motion Correction and Spatiotemporal Dynamic Object Detection

Over the past decade, lidars have become a cornerstone of robotics state estimation and perception thanks to their ability to provide accurate geometric information about their surroundings in the form of 3D scans. Unfortunately, most of nowadays lidars do not take snapshots of the environment but sweep the environment over a period of time (typically around 100 ms). Such a rolling-shutter-like mechanism introduces motion distortion into the collected lidar scan, thus hindering downstream perception applications. In this paper, we present a novel method for motion distortion correction of lidar data by tightly coupling lidar with Inertial Measurement Unit (IMU) data. The motivation of this work is a map-free dynamic object detection based on lidar. The proposed lidar data undistortion method relies on continuous preintegrated of IMU measurements that allow parameterising the sensors' continuous 6-DoF trajectory using solely eleven discrete state variables (biases, initial velocity, and gravity direction). The undistortion consists of feature-based distance minimisation of point-to-line and point-to-plane residuals in a non-linear least-square formulation. Given undistorted geometric data over a short temporal window, the proposed pipeline computes the spatiotemporal normal vector of each of the lidar points. The temporal component of the normals is a proxy for the corresponding point's velocity, therefore allowing for learning-free dynamic object classification without the need for registration in a global reference frame. We demonstrate the soundness of the proposed method and its different components using public datasets and compare them with state-of-the-art lidar-inertial state estimation and dynamic object detection algorithms.

2FAST-2LAMAA: A Lidar-Inertial Localisation and Mapping Framework for Non-Static Environments

This document presents a framework for lidar-inertial localisation and mapping named 2Fast-2Lamaa. The method revolves around two main steps which are the inertial-aided undistortion of the lidar data and the scan-to-map registration using a distance-field representation of the environment. The initialisation-free undistortion uses inertial data to constrain the continuous trajectory of the sensor during the lidar scan. The eleven DoFs that fully characterise the trajectory are estimated by minimising lidar point-to-line and point-to-plane distances in a non-linear least-square formulation. The registration uses a map that provides a distance field for the environment based on Gaussian Process regression. The pose of an undistorted lidar scan is optimised to minimise the distance field queries of its points with respect to the map. After registration, the new geometric information is efficiently integrated into the map. The soundness of 2Fast-2Lamaa is demonstrated over several datasets (qualitative evaluation only). The real-time implementation is made publicly available at https://github.com/UTS-RI/2fast2lamaa.

Towards Robust Perception for Assistive Robotics: An RGB-Event-LiDAR Dataset and Multi-Modal Detection Pipeline

The increasing adoption of human-robot interaction presents opportunities for technology to positively impact lives, particularly those with visual impairments, through applications such as guide-dog-like assistive robotics. We present a pipeline exploring the perception and "intelligent disobedience" required by such a system. A dataset of two people moving in and out of view has been prepared to compare RGB-based and event-based multi-modal dynamic object detection using LiDAR data for 3D position localisation. Our analysis highlights challenges in accurate 3D localisation using 2D image-LiDAR fusion, indicating the need for further refinement. Compared to the performance of the frame-based detection algorithm utilised (YOLOv4), current cutting-edge event-based detection models appear limited to contextual scenarios, such as for automotive platforms. This is highlighted by weak precision and recall over varying confidence and Intersection over Union (IoU) thresholds when using frame-based detections as a ground truth. Therefore, we have publicly released this dataset to the community, containing RGB, event, point cloud and Inertial Measurement Unit (IMU) data along with ground truth poses for the two people in the scene to fill a gap in the current landscape of publicly available datasets and provide a means to assist in the development of safer and more robust algorithms in the future: https://uts-ri.github.io/revel/.

Safe Bubble Cover for Motion Planning on Distance Fields

We consider the problem of planning collision-free trajectories on distance fields. Our key observation is that querying a distance field at one configuration reveals a region of safe space whose radius is given by the distance value, obviating the need for additional collision checking within the safe region. We refer to such regions as safe bubbles, and show that safe bubbles can be obtained from any Lipschitz-continuous safety constraint. Inspired by sampling-based planning algorithms, we present three algorithms for constructing a safe bubble cover of free space, named bubble roadmap (BRM), rapidly exploring bubble graph (RBG), and expansive bubble graph (EBG). The bubble sampling algorithms are combined with a hierarchical planning method that first computes a discrete path of bubbles, followed by a continuous path within the bubbles computed via convex optimization. Experimental results show that the bubble-based methods yield up to 5- 10 times cost reduction relative to conventional baselines while simultaneously reducing computational efforts by orders of magnitude.

VDB-GPDF: Online Gaussian Process Distance Field with VDB Structure

Robots reason about the environment through dedicated representations. Popular choices for dense representations exploit Truncated Signed Distance Functions (TSDF) and Octree data structures. However, TSDF is a projective signed distance obtained directly from depth measurements that overestimates the Euclidean distance. Octrees, despite being memory efficient, require tree traversal and can lead to increased runtime in large scenarios. Other representations based on Gaussian Process (GP) distance fields are appealing due to their probabilistic and continuous nature, but the computational complexity is a concern. In this paper, we present an online efficient mapping framework that seamlessly couples GP distance fields and the fast-access VDB data structure. This framework incrementally builds the Euclidean distance field and fuses other surface properties, like intensity or colour, into a global scene representation that can cater for large-scale scenarios. The key aspect is a latent Local GP Signed Distance Field (L-GPDF) contained in a local VDB structure that allows fast queries of the Euclidean distance, surface properties and their uncertainties for arbitrary points in the field of view. Probabilistic fusion is then performed by merging the inferred values of these points into a global VDB structure that is efficiently maintained over time. After fusion, the surface mesh is recovered, and a global GP Signed Distance Field (G-GPDF) is generated and made available for downstream applications to query accurate distance and gradients. A comparison with the state-of-the-art frameworks shows superior efficiency and accuracy of the inferred distance field and comparable reconstruction performance. The accompanying code will be publicly available. https://github.com/UTS-RI/VDB_GPDF

Dynamic Object Detection in Range data using Spatiotemporal Normals

On the journey to enable robots to interact with the real world where humans, animals, and unpredictable elements are acting as independent agents; it is crucial for robots to have the capability to detect dynamic objects. In this paper, we argue that the detection of dynamic objects can be solved by computing the spatiotemporal normals of a point cloud. In our experiments, we demonstrate that this simple method can be used robustly for LiDAR and depth cameras with performances similar to the state of the art while offering a significantly simpler method.

Continuous-Time Gaussian Process Motion-Compensation for Event-vision Pattern Tracking with Distance Fields

This work addresses the issue of motion compensation and pattern tracking in event camera data. An event camera generates asynchronous streams of events triggered independently by each of the pixels upon changes in the observed intensity. Providing great advantages in low-light and rapid-motion scenarios, such unconventional data present significant research challenges as traditional vision algorithms are not directly applicable to this sensing modality. The proposed method decomposes the tracking problem into a local SE(2) motion-compensation step followed by a homography registration of small motion-compensated event batches. The first component relies on Gaussian Process (GP) theory to model the continuous occupancy field of the events in the image plane and embed the camera trajectory in the covariance kernel function. In doing so, estimating the trajectory is done similarly to GP hyperparameter learning by maximising the log marginal likelihood of the data. The continuous occupancy fields are turned into distance fields and used as templates for homography-based registration. By benchmarking the proposed method against other state-of-the-art techniques, we show that our open-source implementation performs high-accuracy motion compensation and produces high-quality tracks in real-world scenarios.

Accurate Gaussian Process Distance Fields with applications to Echolocation and Mapping

This paper introduces a novel method to estimate distance fields from noisy point clouds using Gaussian Process (GP) regression. Distance fields, or distance functions, gained popularity for applications like point cloud registration, odometry, SLAM, path planning, shape reconstruction, etc. A distance field provides a continuous representation of the scene. It is defined as the shortest distance from any query point and the closest surface. The key concept of the proposed method is a reverting function used to turn a GP-inferred occupancy field into an accurate distance field. The reverting function is specific to the chosen GP kernel. This paper provides the theoretical derivation of the proposed method and its relationship to existing techniques. The improved accuracy compared with existing distance fields is demonstrated with extensive simulated experiments. The level of accuracy of the proposed approach allows for novel applications that rely on precise distance estimation. Thus, alongside 3D point cloud registration, this work presents echolocation and mapping frameworks using ultrasonic guided waves sensing metallic structures. These methods leverage the proposed distance field in physics-based models to simulate the signal propagation and compare it with the actual signal received. Both simulated and real-world experiments are conducted to demonstrate the soundness of these frameworks.

GPGM-SLAM: a Robust SLAM System for Unstructured Planetary Environments with Gaussian Process Gradient Maps

Simultaneous Localization and Mapping (SLAM) techniques play a key role towards long-term autonomy of mobile robots due to the ability to correct localization errors and produce consistent maps of an environment over time. Contrarily to urban or man-made environments, where the presence of unique objects and structures offer unique cues for localization, the appearance of unstructured natural environments is often ambiguous and self-similar, hindering the performances of loop closure detection. In this paper, we present an approach to improve the robustness of place recognition in the context of a submap-based stereo SLAM based on Gaussian Process Gradient Maps (GPGMaps). GPGMaps embed a continuous representation of the gradients of the local terrain elevation by means of Gaussian Process regression and Structured Kernel Interpolation, given solely noisy elevation measurements. We leverage the image-like structure of GPGMaps to detect loop closures using traditional visual features and Bag of Words. GPGMap matching is performed as an SE(2) alignment to establish loop closure constraints within a pose graph. We evaluate the proposed pipeline on a variety of datasets recorded on Mt. Etna, Sicily and in the Morocco desert, respectively Moon- and Mars-like environments, and we compare the localization performances with state-of-the-art approaches for visual SLAM and visual loop closure detection.