Online 6DoF Pose Estimation in Forests using Cross-View Factor Graph Optimisation and Deep Learned Re-localisation

This paper presents a novel approach for robust global localisation and 6DoF pose estimation of ground robots in forest environments by leveraging cross-view factor graph optimisation and deep-learned re-localisation. The proposed method addresses the challenges of aligning aerial and ground data for pose estimation, which is crucial for accurate point-to-point navigation in GPS-denied environments. By integrating information from both perspectives into a factor graph framework, our approach effectively estimates the robot's global position and orientation. We validate the performance of our method through extensive experiments in diverse forest scenarios, demonstrating its superiority over existing baselines in terms of accuracy and robustness in these challenging environments. Experimental results show that our proposed localisation system can achieve drift-free localisation with bounded positioning errors, ensuring reliable and safe robot navigation under canopies.

Towards Long-term Robotics in the Wild

In this paper, we emphasise the critical importance of large-scale datasets for advancing field robotics capabilities, particularly in natural environments. While numerous datasets exist for urban and suburban settings, those tailored to natural environments are scarce. Our recent benchmarks WildPlaces and WildScenes address this gap by providing synchronised image, lidar, semantic and accurate 6-DoF pose information in forest-type environments. We highlight the multi-modal nature of this dataset and discuss and demonstrate its utility in various downstream tasks, such as place recognition and 2D and 3D semantic segmentation tasks.

WildScenes: A Benchmark for 2D and 3D Semantic Segmentation in Large-scale Natural Environments

Recent progress in semantic scene understanding has primarily been enabled by the availability of semantically annotated bi-modal (camera and lidar) datasets in urban environments. However, such annotated datasets are also needed for natural, unstructured environments to enable semantic perception for applications, including conservation, search and rescue, environment monitoring, and agricultural automation. Therefore, we introduce WildScenes, a bi-modal benchmark dataset consisting of multiple large-scale traversals in natural environments, including semantic annotations in high-resolution 2D images and dense 3D lidar point clouds, and accurate 6-DoF pose information. The data is (1) trajectory-centric with accurate localization and globally aligned point clouds, (2) calibrated and synchronized to support bi-modal inference, and (3) containing different natural environments over 6 months to support research on domain adaptation. Our 3D semantic labels are obtained via an efficient automated process that transfers the human-annotated 2D labels from multiple views into 3D point clouds, thus circumventing the need for expensive and time-consuming human annotation in 3D. We introduce benchmarks on 2D and 3D semantic segmentation and evaluate a variety of recent deep-learning techniques to demonstrate the challenges in semantic segmentation in natural environments. We propose train-val-test splits for standard benchmarks as well as domain adaptation benchmarks and utilize an automated split generation technique to ensure the balance of class label distributions. The data, evaluation scripts and pretrained models will be released upon acceptance at https://csiro-robotics.github.io/WildScenes.

Pose-Graph Attentional Graph Neural Network for Lidar Place Recognition

This paper proposes a lidar place recognition approach, called P-GAT, to increase the receptive field between point clouds captured over time. Instead of comparing pairs of point clouds, we compare the similarity between sets of point clouds to use the maximum spatial and temporal information between neighbour clouds utilising the concept of pose-graph SLAM. Leveraging intra- and inter-attention and graph neural network, P-GAT relates point clouds captured in nearby locations in Euclidean space and their embeddings in feature space. Experimental results on the large-scale publically available datasets demonstrate the effectiveness of our approach in recognising scenes lacking distinct features and when training and testing environments have different distributions (domain adaptation). Further, an exhaustive comparison with the state-of-the-art shows improvements in performance gains. Code will be available upon acceptance.

Demonstrating Autonomous 3D Path Planning on a Novel Scalable UGV-UAV Morphing Robot

Some animals exhibit multi-modal locomotion capability to traverse a wide range of terrains and environments, such as amphibians that can swim and walk or birds that can fly and walk. This capability is extremely beneficial for expanding the animal's habitat range and they can choose the most energy efficient mode of locomotion in a given environment. The robotic biomimicry of this multi-modal locomotion capability can be very challenging but offer the same advantages. However, the expanded range of locomotion also increases the complexity of performing localization and path planning. In this work, we present our morphing multi-modal robot, which is capable of ground and aerial locomotion, and the implementation of readily available SLAM and path planning solutions to navigate a complex indoor environment.

Deep Robust Multi-Robot Re-localisation in Natural Environments

The success of re-localisation has crucial implications for the practical deployment of robots operating within a prior map or relative to one another in real-world scenarios. Using single-modality, place recognition and localisation can be compromised in challenging environments such as forests. To address this, we propose a strategy to prevent lidar-based re-localisation failure using lidar-image cross-modality. Our solution relies on self-supervised 2D-3D feature matching to predict alignment and misalignment. Leveraging a deep network for lidar feature extraction and relative pose estimation between point clouds, we train a model to evaluate the estimated transformation. A model predicting the presence of misalignment is learned by analysing image-lidar similarity in the embedding space and the geometric constraints available within the region seen in both modalities in Euclidean space. Experimental results using real datasets (offline and online modes) demonstrate the effectiveness of the proposed pipeline for robust re-localisation in unstructured, natural environments.

Heterogeneous robot teams with unified perception and autonomy: How Team CSIRO Data61 tied for the top score at the DARPA Subterranean Challenge

The DARPA Subterranean Challenge was designed for competitors to develop and deploy teams of autonomous robots to explore difficult unknown underground environments. Categorised in to human-made tunnels, underground urban infrastructure and natural caves, each of these subdomains had many challenging elements for robot perception, locomotion, navigation and autonomy. These included degraded wireless communication, poor visibility due to smoke, narrow passages and doorways, clutter, uneven ground, slippery and loose terrain, stairs, ledges, overhangs, dripping water, and dynamic obstacles that move to block paths among others. In the Final Event of this challenge held in September 2021, the course consisted of all three subdomains. The task was for the robot team to perform a scavenger hunt for a number of pre-defined artefacts within a limited time frame. Only one human supervisor was allowed to communicate with the robots once they were in the course. Points were scored when accurate detections and their locations were communicated back to the scoring server. A total of 8 teams competed in the finals held at the Mega Cavern in Louisville, KY, USA. This article describes the systems deployed by Team CSIRO Data61 that tied for the top score and won second place at the event.

Wild-Places: A Large-Scale Dataset for Lidar Place Recognition in Unstructured Natural Environments

Many existing datasets for lidar place recognition are solely representative of structured urban environments, and have recently been saturated in performance by deep learning based approaches. Natural and unstructured environments present many additional challenges for the tasks of long-term localisation but these environments are not represented in currently available datasets. To address this we introduce Wild-Places, a challenging large-scale dataset for lidar place recognition in unstructured, natural environments. Wild-Places contains eight lidar sequences collected with a handheld sensor payload over the course of fourteen months, containing a total of 67K undistorted lidar submaps along with accurate 6DoF ground truth. Our dataset contains multiple revisits both within and between sequences, allowing for both intra-sequence (i.e. loop closure detection) and inter-sequence (i.e. re-localisation) place recognition. We also benchmark several state-of-the-art approaches to demonstrate the challenges that this dataset introduces, particularly the case of long-term place recognition due to natural environments changing over time. Our dataset and code will be available at https://csiro-robotics.github.io/Wild-Places.

Uncertainty-Aware Lidar Place Recognition in Novel Environments

State-of-the-art approaches to lidar place recognition degrade significantly when tested on novel environments that are not present in their training dataset. To improve their reliability, we propose uncertainty-aware lidar place recognition, where each predicted place match must have an associated uncertainty that can be used to identify and reject potentially incorrect matches. We introduce a novel evaluation protocol designed to benchmark uncertainty-aware lidar place recognition, and present Deep Ensembles as the first uncertainty-aware approach for this task. Testing across three large-scale datasets and three state-of-the-art architectures, we show that Deep Ensembles consistently improves the performance of lidar place recognition in novel environments. Compared to a standard network, our results show that Deep Ensembles improves the Recall@1 by more than 5% and AuPR by more than 3% on average when tested on previously unseen environments. Our code repository will be made publicly available upon paper acceptance at https://github.com/csiro-robotics/Uncertainty-LPR.

A Letter on Progress Made on Husky Carbon: A Legged-Aerial, Multi-modal Platform

Animals, such as birds, widely use multi-modal locomotion by combining legged and aerial mobility with dominant inertial effects. The robotic biomimicry of this multi-modal locomotion feat can yield ultra-flexible systems in terms of their ability to negotiate their task spaces. The main objective of this paper is to discuss the challenges in achieving multi-modal locomotion, and to report our progress in developing our quadrupedal robot capable of multi-modal locomotion (legged and aerial locomotion), the Husky Carbon. We report the mechanical and electrical components utilized in our robot, in addition to the simulation and experimentation done to achieve our goal in developing a versatile multi-modal robotic platform.