Efficient Dense Frontier Detection for 2-D Graph SLAM Based on Occupancy Grid Submaps

Oršulić, Juraj; Miklić, Damjan; Kovačić, Zdenko

doi:10.1109/lra.2019.2928203

Cited by 28 publications

(13 citation statements)

References 16 publications

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“…The SLAM algorithm plays an important role in many applications, such as civil infrastructure surveys, search and rescue, and autonomous exploration. Cartographer provides a map suitable for both 2D [12], [13] and 3D [10] autonomous exploration, employing the well known frontier detection method for exploration planning and execution.…”

Section: Navigationmentioning

confidence: 99%

Flying with Cartographer: Adapting the Cartographer 3D Graph SLAM Stack for UAV Navigation

Oršulić

Milijas

Batinović

et al. 2021

2021 Aerial Robotic Systems Physically Interacting With the Environment (AIRPHARO)

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This paper describes an application of the Cartographer graph SLAM stack as a pose sensor in a UAV feedback control loop, with certain application-specific changes in the SLAM stack such as smoothing of the optimized pose. Pose estimation is performed by fusing 3D LiDAR/IMU-based proprioception with GPS position measurements by means of pose graph optimisation. Moreover, partial environment maps built from the LiDAR data (submaps) within the Cartographer SLAM stack are marshalled into OctoMap, an Octree-based voxel map implementation. The OctoMap is further used for navigation tasks such as path planning and obstacle avoidance.

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Section: Navigationmentioning

confidence: 99%

Flying with Cartographer: Adapting the Cartographer 3D Graph SLAM Stack for UAV Navigation

Oršulić

Milijas

Batinović

et al. 2021

2021 Aerial Robotic Systems Physically Interacting With the Environment (AIRPHARO)

Self Cite

View full text Add to dashboard Cite

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“…Thus, it consumes more massive computational resources as compared with smaller maps. In [12][13][14][15], they introduced a simplified map by using a grid-based map. For every scan of the sensor, first, the data are down sampled in order to reduce map information.…”

Section: Related Workmentioning

confidence: 99%

Cartographer SLAM Method for Optimization with an Adaptive Multi-Distance Scan Scheduler

et al. 2020

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This paper presents the use of Google's simultaneous localization and mapping (SLAM) technique, namely Cartographer, and adaptive multistage distance scheduler (AMDS) to improve the processing speed. This approach optimizes the processing speed of SLAM which is known to have performance degradation as the map grows due to a larger scan matcher. In this proposed work, the adaptive method was successfully tested in an actual vehicle to map roads in real time. The AMDS performs a local pose correction by controlling the LiDAR sensor scan range and scan matcher search window with the help of scheduling algorithms. The scheduling algorithms manage the SLAM that swaps between short and long distances during map data collection. As a result, the algorithms efficiently improved performance speed similar to short distance LiDAR scans while maintaining the accuracy of the full distance of LiDAR. By swapping the scan distance of the sensor, and adaptively limiting the search size of the scan matcher to handle difference scan sizes, the pose's generation performance time is improved by approximately 16% as compared with a fixed scan distance, while maintaining similar accuracy.

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“…When a set of frontier cells is detected, and the robot passes through that area, all of the tree branches are then removed to free up memory and speed up the calculation process. Orsulic et al, (2019) propose a method of frontier detection for a multi-robot scenario, where each of them performs frontier detection separately in local maps. A global map is then constructed to combine the information from all local maps with all of the frontiers.…”

Section: Introductionmentioning

confidence: 99%

Approaches for Efficiently Detecting Frontier Cells in Robotics Exploration

Quin

Nguyen

et al. 2021

Front. Robot. AI

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Many robot exploration algorithms that are used to explore office, home, or outdoor environments, rely on the concept of frontier cells. Frontier cells define the border between known and unknown space. Frontier-based exploration is the process of repeatedly detecting frontiers and moving towards them, until there are no more frontiers and therefore no more unknown regions. The faster frontier cells can be detected, the more efficient exploration becomes. This paper proposes several algorithms for detecting frontiers. The first is called Naïve Active Area (NaïveAA) frontier detection and achieves frontier detection in constant time by only evaluating the cells in the active area defined by scans taken. The second algorithm is called Expanding-Wavefront Frontier Detection (EWFD) and uses frontiers from the previous timestep as a starting point for searching for frontiers in newly discovered space. The third approach is called Frontier-Tracing Frontier Detection (FTFD) and also uses the frontiers from the previous timestep as well as the endpoints of the scan, to determine the frontiers at the current timestep. Algorithms are compared to state-of-the-art algorithms such as Naïve, WFD, and WFD-INC. NaïveAA is shown to operate in constant time and therefore is suitable as a basic benchmark for frontier detection algorithms. EWFD and FTFD are found to be significantly faster than other algorithms.

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Efficient Dense Frontier Detection for 2-D Graph SLAM Based on Occupancy Grid Submaps

Cited by 28 publications

References 16 publications

Flying with Cartographer: Adapting the Cartographer 3D Graph SLAM Stack for UAV Navigation

Flying with Cartographer: Adapting the Cartographer 3D Graph SLAM Stack for UAV Navigation

Cartographer SLAM Method for Optimization with an Adaptive Multi-Distance Scan Scheduler

Approaches for Efficiently Detecting Frontier Cells in Robotics Exploration

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