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Autonomous navigation in unstructured vegetated environments remains an open challenge. To successfully operate in these settings, autonomous ground vehicles (AGVs) must assess the environment and determine which vegetation is pliable enough to safely traverse. In this paper, we propose ForestTrav (Forest Traversability): a novel lidar-only (geometric), online traversability estimation (TE) method that can accurately generate a per-voxel traversability estimate for densely vegetated environments, demonstrated in dense subtropical forests. The method leverages a salient, probabilistic 3D voxel representation, continuously fusing incoming lidar measurements to maintain multiple, per-voxel ray statistics, in combination with the structural context and compactness of sparse convolutional neural networks (SCNNs) to perform accurate TE in densely vegetated environments. The proposed method is real-time capable and is shown to outperform state-of-the-art volumetric and 2.5D TE methods by a significant margin (0.62 vs. 0.41 Matthews correlation coefficient (MCC) score at qty 0.1 m voxel resolution) in challenging scenes and to generalize to unseen environments. ForestTrav demonstrates that lidar-only (geometric) methods can provide accurate, online TE in complex, densely-vegetated environments. This capability has not been previously demonstrated in the literature in such complex environments. Further, we analyze the response of the TE methods to the temporal and spatial evolution of the probabilistic map as a function of information accumulated over time during scene exploration. It shows that our method performs well even with limited information in the early stages of exploration, and this provides an additional tool to assess the expected performance during deployment. Finally, to train and assess TE methods in highly-vegetated environments, we collected and labeled a novel, real-world data set and provide it to the community as an open-source resource.

This paper describes a novel autonomous ground vehicle that is designed for exploring unknown environments which contain sources of ionising radiation, such as might be found in a nuclear disaster site or a legacy nuclear facility. While exploring the environment, it is important that the robot avoids radiation hot spots to minimise breakdowns. Broken down robots present a real problem: they not only cause the mission to fail but they can block access routes for future missions. Until now, such robots have had no autonomous gamma radiation avoidance capabilities. New software algorithms are presented that allow radiation measurements to be converted into a format in which they can be integrated into the robot’s navigation system so that it can actively avoid receiving a high radiation dose during a mission. An unmanned ground vehicle was fitted with a gamma radiation detector and an autonomous navigation package that included the new radiation avoidance software. The full system was evaluated experimentally in a complex semi-structured environment that contained two radiation sources. In the experiment, the robot successfully identified both sources and avoided areas that were found to have high levels of radiation while navigating between user defined waypoints. This advancement in the state-of-the-art has the potential to deliver real benefit to the nuclear industry, in terms of both increased chance of mission success and reduction of the reliance on human operatives to perform tasks in dangerous radiation environments.

Light Detection and Ranging (LiDAR), a comparatively new technology in the field of underwater surveying, has principally been used for taking precise measurement of undersea structures in the oil and gas industry. Typically, the LiDAR is deployed on a remotely operated vehicle (ROV), which will “land” on the seafloor in order to generate a 3D point cloud of its environment from a stationary position. To explore the potential of subsea LiDAR on a moving platform in an environmental context, we deployed an underwater LiDAR system simultaneously with a multibeam echosounder (MBES), surveying Kingston Reef off the coast of Rottnest Island, Western Australia. This paper compares and summarises the relative accuracy and characteristics of underwater LiDAR and multibeam sonar and investigates synergies between sonar and LiDAR technology for the purpose of benthic habitat mapping and underwater simultaneous localisation and mapping (SLAM) for Autonomous Underwater Vehicles (AUVs). We found that LiDAR reflectivity and multibeam backscatter are complementary technologies for habitat mapping, which can combine to discriminate between habitats that could not be mapped with either one alone. For robot navigation, SLAM can be effectively applied with either technology, however, when a Global Navigation Satellite System (GNSS) is available, SLAM does not significantly improve the self-consistency of multibeam data, but it does for LiDAR.