Autonomous Navigation
130 papers with code • 0 benchmarks • 5 datasets
Autonomous navigation is the task of autonomously navigating a vehicle or robot to or around a location without human guidance.
( Image credit: Approximate LSTMs for Time-Constrained Inference: Enabling Fast Reaction in Self-Driving Cars )
Benchmarks
These leaderboards are used to track progress in Autonomous Navigation
RELLIS-3D
JRDB
IN2LAAMA
Latest papers
Achelous++: Power-Oriented Water-Surface Panoptic Perception Framework on Edge Devices based on Vision-Radar Fusion and Pruning of Heterogeneous Modalities
Urban water-surface robust perception serves as the foundation for intelligent monitoring of aquatic environments and the autonomous navigation and operation of unmanned vessels, especially in the context of waterway safety.
A Data-efficient Framework for Robotics Large-scale LiDAR Scene Parsing
More importantly, we innovatively propose to learn to merge the over-divided clusters based on the local low-level geometric property similarities and the learned high-level feature similarities supervised by weak labels.
OpenStereo: A Comprehensive Benchmark for Stereo Matching and Strong Baseline
Based on OpenStereo, we conducted experiments and have achieved or surpassed the performance metrics reported in the original paper.
JRDB-Traj: A Dataset and Benchmark for Trajectory Forecasting in Crowds
To address this, we introduce a novel dataset for end-to-end trajectory forecasting, facilitating the evaluation of models in scenarios involving less-than-ideal preceding modules such as tracking.
Streaming Motion Forecasting for Autonomous Driving
ziqipang/streamingforecasting
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Our benchmark inherently captures the disappearance and re-appearance of agents, presenting the emergent challenge of forecasting for occluded agents, which is a safety-critical problem yet overlooked by snapshot-based benchmarks.
Learning to Terminate in Object Navigation
huskykingdom/dita_acml2023
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This paper tackles the critical challenge of object navigation in autonomous navigation systems, particularly focusing on the problem of target approach and episode termination in environments with long optimal episode length in Deep Reinforcement Learning (DRL) based methods.
Towards Robust Robot 3D Perception in Urban Environments: The UT Campus Object Dataset
Using our dataset and annotations, we release benchmarks for 3D object detection and 3D semantic segmentation using established metrics.
URA*: Uncertainty-aware Path Planning using Image-based Aerial-to-Ground Traversability Estimation for Off-road Environments
Results show that the proposed image segmentation and planning methods outperform conventional planning algorithms in terms of the quality and feasibility of the initial path, as well as the quality of replanned paths.
Compressing Vision Transformers for Low-Resource Visual Learning
In our work, we aim to take a step toward bringing vision transformers to the edge by utilizing popular model compression techniques such as distillation, pruning, and quantization.
SUMMIT: Source-Free Adaptation of Uni-Modal Models to Multi-Modal Targets
In this work, we relax both of these assumptions by addressing the problem of adapting a set of models trained independently on uni-modal data to a target domain consisting of unlabeled multi-modal data, without having access to the original source dataset.
