Campus Autonomous Robot Tours

AbstractUnderstanding the scene in which an autonomous robot operates is critical for its competent functioning. Such scene comprehension necessitates recognizing instances of traffic participants along with general scene semantics which can be effectively addressed by the panoptic segmentation task. In this paper, we introduce the Efficient Panoptic Segmentation (EfficientPS) architecture that consists of a shared backbone which efficiently encodes and fuses semantically rich multi-scale features. We incorporate a new semantic head that aggregates fine and contextual features coherently and a new variant of Mask R-CNN as the instance head. We also propose a novel panoptic fusion module that congruously integrates the output logits from both the heads of our EfficientPS architecture to yield the final panoptic segmentation output. Additionally, we introduce the KITTI panoptic segmentation dataset that contains panoptic annotations for the popularly challenging KITTI benchmark. Extensive evaluations on Cityscapes, KITTI, Mapillary Vistas and Indian Driving Dataset demonstrate that our proposed architecture consistently sets the new state-of-the-art on all these four benchmarks while being the most efficient and fast panoptic segmentation architecture to date.

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Spiking Neural Controller for Autonomous Robot Navigation in Dynamic Environments

2020 10th International Conference on Computer and Knowledge Engineering (ICCKE) ◽

10.1109/iccke50421.2020.9303687 ◽

2020 ◽

Author(s):

Mohammad Tayefe Ramezanlou ◽

Vahid Azimirad ◽

Saleh Valizadeh Sotubadi ◽

Farrokh Janabi-Sharifi

Keyword(s):

Robot Navigation ◽

Dynamic Environments ◽

Autonomous Robot ◽

Neural Controller ◽

Autonomous Robot Navigation

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Safe Path Planning Algorithms for Mobile Robots Based on Probabilistic Foam

Sensors ◽

10.3390/s21124156 ◽

2021 ◽

Vol 21 (12) ◽

pp. 4156

Author(s):

Luís B. P. Nascimento ◽

Dennis Barrios-Aranibar ◽

Vitor G. Santos ◽

Diego S. Pereira ◽

William C. Ribeiro ◽

...

Keyword(s):

Path Planning ◽

Mobile Robots ◽

Configuration Space ◽

Autonomous Robot ◽

Breadth First Search ◽

Propagation Process ◽

Robot Systems ◽

And Performance ◽

Safe Path ◽

High Clearance

The planning of safe paths is an important issue for autonomous robot systems. The Probabilistic Foam method (PFM) is a planner that guarantees safe paths bounded by a sequence of structures called bubbles that provides safe regions. This method performs the planning by covering the free configuration space with bubbles, an approach analogous to a breadth-first search. To improve the propagation process and keep the safety, we present three algorithms based on Probabilistic Foam: Goal-biased Probabilistic Foam (GBPF), Radius-biased Probabilistic Foam (RBPF), and Heuristic-guided Probabilistic Foam (HPF); the last two are proposed in this work. The variant GBPF is fast, HPF finds short paths, and RBPF finds high-clearance paths. Some simulations were performed using four different maps to analyze the behavior and performance of the methods. Besides, the safety was analyzed considering the new propagation strategies.

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