UFOMap: An Efficient Probabilistic 3D Mapping Framework That Embraces the Unknown

Duberg, Daniel

doi:10.1109/lra.2020.3013861

Cited by 71 publications

(39 citation statements)

References 30 publications

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“…Pruning makes the map more memory efficient and faster to search in compared to a naive implementation of an occupancy grid since it reduces the number of nodes without losing any information about the environment. Duberg and Jensfelt [2] build on the work of Wurm et al [1] and models unknown space explicitly which is suitable for exploration where unknown space often is accessed. Their implementation is also faster to manipulate than Octomap.…”

Section: Octree Occupancy Mapmentioning

confidence: 99%

“…The assumption that the environment is static when exploring but dynamic when not exploring is made as the proposed solution models the probability that a voxel changes its state between visits of a scene. The map used are based on an octree occupancy grid developed by Duberg and Jensfelt [2] as described in section 2.1.1 but contains further information on the dynamics of the environment based on the work of Saarinen, Andreasson, and Lilienthal [3].…”

Section: Dynamical Map Representationmentioning

confidence: 99%

“…, as described in [3]. UFOMap [2] is also modified to accommodate the modeling of dynamics. Pruning, the merging of children in the tree based on their similarities, is turned off.…”

Section: Dynamic Map Modelingmentioning

confidence: 99%

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Long-Term Exploration in Unknown Dynamic Environments

Bonnevie

Duberg

2021

2021 7th International Conference on Automation, Robotics and Applications (ICARA)

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In order for autonomous robots to perform tasks and safely navigate environments they need to have a reliable and detailed map. These maps are generally created by the robot itself since maps with the required level of detail rarely exist beforehand. In order to create that map the robot has to explore an unknown environment. Such activity is referred to as autonomous exploration within the field of robotics. Most research done in autonomous exploration assumes a static environment. Since most environments in the real world often changes over time an exploration algorithm that is able to re-explore areas where changes may occur is of interest for autonomous long term missions.This thesis presents a method to predict where changes may occur in the environment using Markov chains and an occupancy grid map. An exploration algorithm is also developed with the aim of keeping an updated map of a changing environment. The exploration algorithm is based on a static exploration algorithm that uses RRT to sample poses and evaluates these poses based on the length of the path to get there and the information gain at and on the path to the sampled pose.An evaluation of both the mapping and exploration is made respectively. The mapping is evaluated on its ability of suppressing noisy measurements whilst being able to accurately model the dynamics of the map. The exploration algorithm is evaluated in three different environments of increasing complexity. Its ability to seek out areas susceptible of change whilst providing data for the mapping is evaluated in each environment. The results show both a mapping and exploration algorithm who works well but are noise sensitive.I would like to thank my supervisor Daniel Duberg for all the support that he has given me throughout this project. Thank you Fernando dos Santos Barbosa for acting as a second supervisor, and a big thanks to Viktor Kull for helping me learn C++ for and shortening my debug-time by a lot.

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Section: Octree Occupancy Mapmentioning

confidence: 99%

Section: Dynamical Map Representationmentioning

confidence: 99%

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Long-Term Exploration in Unknown Dynamic Environments

Bonnevie

Duberg

2021

2021 7th International Conference on Automation, Robotics and Applications (ICARA)

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“…In the literature, Octomap [18] and UfoMap [10] are two open-source approaches that can be used to implement a map from the point cloud data. In [10], it is shown that UfoMap is more advantageous to represent the unknown region, more memory efficient and achieves faster insertion times than Octomap. However, we implement our local map as the Octomap structure for two reasons.…”

Section: Local Mapmentioning

confidence: 99%

“…The nearby clutter value Π clut j is calculated for each trajectory j as in Eq. (10). Here, the first variable Ω tot j equals to the sum of all Priority and Support voxel weights β i .…”

Section: Nearby Cluttermentioning

confidence: 99%

Reactive Navigation Framework for Mobile Robots by Heuristically Evaluated Pre-sampled Trajectories

Akmandor,

Padır

2021

Preprint

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This paper describes and analyzes a reactive navigation framework for mobile robots in unknown environments. The approach does not rely on a global map and only considers the local occupancy in its robotcentered 3D grid structure. The proposed algorithm enables fast navigation by heuristic evaluations of pre-sampled trajectories on-the-fly. At each cycle, these paths are evaluated by a weighted cost function, based on heuristic features such as closeness to the goal, previously selected trajectories, and nearby obstacles. This paper introduces a systematic method to calculate a feasible pose on the selected trajectory, before sending it to the controller for the motion execution. Defining the structures in the framework and providing the implementation details, the paper also explains how to adjust its offline and online parameters. To demonstrate the versatility and adaptability of the algorithm in unknown environments, physics-based simulations on various maps are presented. Benchmark tests show the superior performance of the proposed algorithm over its previous iteration and another state-of-art method. The open-source implementation of the algorithm and the benchmark data can be found at https://github.com/RIVeR-Lab/tentabot.

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A comprehensive survey on non‐cooperative collision avoidance for micro aerial vehicles: Sensing and obstacle detection

Lü

Fasano

Carrio³

et al. 2023

Journal of Field Robotics

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In recent years, unmanned aerial vehicles (UAVs) have been confirmed as a powerful tool for countless applications in nearly every industry, in which collision avoidance plays a vital role for micro aerial vehicles (MAVs) performing autonomous missions.The complexity of collision avoidance systems is directly related to the complexity of the navigation environment, the avoidance of highly uncertain obstacles being a more thoroughly researched critical aspect. The non-cooperative collision avoidance strategy is necessary when communication between the agents is unavailable, under the case with many existing passive obstacles and non-cooperative flying objects. In this article, we review the topic of non-cooperative collision avoidance for MAVs in dynamic environments by covering two main issues: onboard sensing and obstacle description algorithms. A detailed discussion of these two topics is provided to face non-cooperative collision avoidance technology for MAVs. Finally, the paper summarizes the open challenges and foreseen future directions in this field.

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UFOMap: An Efficient Probabilistic 3D Mapping Framework That Embraces the Unknown

Cited by 71 publications

References 30 publications

Long-Term Exploration in Unknown Dynamic Environments

Long-Term Exploration in Unknown Dynamic Environments

Reactive Navigation Framework for Mobile Robots by Heuristically Evaluated Pre-sampled Trajectories

A comprehensive survey on non‐cooperative collision avoidance for micro aerial vehicles: Sensing and obstacle detection

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