echoe
- North America > United States (0.04)
- Europe > Germany (0.04)
- Asia > China > Shanghai > Shanghai (0.04)
- Research Report > New Finding (0.68)
- Research Report > Experimental Study (0.68)
- Europe > United Kingdom > England (0.16)
- Europe > Norway (0.05)
- Oceania > Australia (0.04)
- (5 more...)
- Banking & Finance > Trading (1.00)
- Transportation > Ground > Road (0.96)
A robot bat sheds new light on how they hunt in darkness
The lesser long-nosed bat (Leptonycteris yerbabuenae) is a medium-sized bat found in Central and North America. Breakthroughs, discoveries, and DIY tips sent six days a week. Biologists and engineers have joined forces to build a new robot bat that's helping us understand how bats use echolocation to hunt for food. By creating a robot that can echolocate, the team mimicked a bat's flight path and explained how bats can quickly determine whether or not their prey is on a leaf. This new bat's eye view is detailed in a study recently published in the The study was led in part by bat scientist and Smithsonian Tropical Research Institute research associate Inga Geipel .
- North America > United States > New York (0.04)
- North America > United States > Illinois (0.04)
- Asia > Middle East > Israel > Tel Aviv District > Tel Aviv (0.04)
Echoes Beyond Points: Unleashing the Power of Raw Radar Data in Multi-modality Fusion
Radar is ubiquitous in autonomous driving systems due to its low cost and good adaptability to bad weather. Nevertheless, the radar detection performance is usually inferior because its point cloud is sparse and not accurate due to the poor azimuth and elevation resolution. Moreover, point cloud generation algorithms already drop weak signals to reduce the false targets which may be suboptimal for the use of deep fusion. In this paper, we propose a novel method named EchoFusion to skip the existing radar signal processing pipeline and then incorporate the radar raw data with other sensors. Specifically, we first generate the Bird's Eye View (BEV) queries and then take corresponding spectrum features from radar to fuse with other sensors. By this approach, our method could utilize both rich and lossless distance and speed clues from radar echoes and rich semantic clues from images, making our method surpass all existing methods on the RADIal dataset, and approach the performance of LiDAR.
- North America > United States (0.28)
- Europe > Switzerland > Vaud > Lausanne (0.04)
- North America > Canada > Quebec > Montreal (0.04)
- (3 more...)
- North America > United States (0.14)
- Europe > Switzerland > Vaud > Lausanne (0.04)
- North America > Canada > Quebec > Montreal (0.04)
- (2 more...)
- North America > United States > New York (0.04)
- North America > United States > Massachusetts (0.04)
- North America > Canada > British Columbia > Metro Vancouver Regional District > Vancouver (0.04)
- (3 more...)
- North America > United States (0.14)
- Europe > Germany (0.04)
- Asia > China > Shanghai > Shanghai (0.04)
- Research Report > New Finding (0.68)
- Research Report > Experimental Study (0.68)
2025 Hugo Award game finalists include Zelda: Echoes of Wisdom and Dragon Age: The Veilguard
The Hugo Awards began honoring video games for the first time back in 2021. This week, the organization revealed the list of six finalists for the 2025 awards ceremony. Let's go over the nominations. Two AAA titles are up for the award. The gameplay involves summoning monsters and items to solve puzzles and do battle.
- Personal > Honors (0.97)
- Summary/Review (0.63)
EDENet: Echo Direction Encoding Network for Place Recognition Based on Ground Penetrating Radar
Zhang, Pengyu, Chen, Xieyuanli, Chen, Yuwei, Bi, Beizhen, Xu, Zhuo, Jin, Tian, Huang, Xiaotao, Shen, Liang
Ground penetrating radar (GPR) based localization has gained significant recognition in robotics due to its ability to detect stable subsurface features, offering advantages in environments where traditional sensors like cameras and LiDAR may struggle. However, existing methods are primarily focused on small-scale place recognition (PR), leaving the challenges of PR in large-scale maps unaddressed. These challenges include the inherent sparsity of underground features and the variability in underground dielectric constants, which complicate robust localization. In this work, we investigate the geometric relationship between GPR echo sequences and underground scenes, leveraging the robustness of directional features to inform our network design. We introduce learn-able Gabor filters for the precise extraction of directional responses, coupled with a direction-aware attention mechanism for effective geometric encoding. To further enhance performance, we incorporate a shift-invariant unit and a multi-scale aggregation strategy to better accommodate variations in dielectric constants. Experiments conducted on public datasets demonstrate that our proposed EDENet not only surpasses existing solutions in terms of PR performance but also offers advantages in model size and computational efficiency.
- Asia > China (0.28)
- North America > United States (0.14)