同时定位和建图(SLAM)是机器人的一项基本任务,驱动着自动驾驶和虚拟现实等众多应用发展。 近年来,神经隐式SLAM(neural implicit SLAM)研究取得了令人印象深刻的成果。 然而,在具有挑战性或数据有限的情况下,神经SLAM(neural SLAM)的鲁棒性仍然不够。 本文提出了一种混合增强的神经SLAM鲁棒优化方法:HERO-SLAM,它结合...
We present an approach for agents to learn representations of a global map from sensor data, to aid their exploration in new environments. To achieve this, we embed procedures mimicking that of tradit, 视频播放量 232、弹幕量 0、点赞数 3、投硬币枚数 2、收
Co-SLAM: Joint Coordinate and Sparse Parametric Encodings for Neural Real-Time SLAM https://hengyiwang.github.io/projects/CoSLAM https://arxiv.org/abs/2304.14377 https://github.com/HengyiWang/Co-SLAM NICE-SLAM: NICE-SLAM:Neural Implicit Scalable Encoding for SLAM https://pengsongyou.github.io...
# Neural SLAM Module if args.train_slam: # Add frames to memory for env_idx in range(num_scenes): env_obs = obs[env_idx].to("cpu") env_poses = torch.from_numpy(np.asarray( infos[env_idx]['sensor_pose'] )).float().to("cpu") ...
git clone --recurse-submodules https://github.com/devendrachaplot/Neural-SLAM cd Neural-SLAM; pip install -r requirements.txt The code requires datasets in adatafolder in the following format (same as habitat-api): Neural-SLAM/ data/
在本文中提出了一种模块化和层次化的方法来学习探索3D环境的策略,称为Active Neural SLAM。本文方法通过learned SLAM模块, 和global、localpolicies分析路径规划,充分整合了经典方法和learning-based方法的优势。使用该学习提供了输入模式方面的灵活性(learned SLAM模块),利用普遍存在的结构规则(global policies),并提供了...
git clone --recurse-submodules https://github.com/devendrachaplot/Neural-SLAM cd Neural-SLAM; pip install -r requirements.txt The code requires datasets in adatafolder in the following format (same as habitat-api): Neural-SLAM/ data/ scene_datasets/ gibson/ Adrian.glb Adrian.navmesh ... da...
Neural implicit representations have shown remarkable abilities in jointly modeling geometry, color, and camera poses in simultaneous localization and mapping (SLAM). Current methods use coordinates, positional encodings, or other geometry features as input to query neural implicit functions for signed ...
Simultaneous Localization and Mapping (SLAM) is a fundamental task in robotics, driving numerous applications such as autonomous driving and virtual reality. Recent progress on neural implicit SLAM has shown encouraging and impressive results. However, the robustness of neural SLAM, particularly in challe...
Figure 1: Visualization of the Neural-SLAM model architecture (we intentionally use blue for the components in charge of computation, green for memory, and cyan for a mixture of both.) We formulate the exploration task as a Markov decision process (MDP) in which the agent interacts with the...