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Found 6 papers, 1 papers with code
Why Sample Space Matters: Keyframe Sampling Optimization for LiDAR-based Place Recognition
We then present a novel keyframe sampling approach for LiDAR-based place recognition, which focuses on redundancy minimization and information preservation in the hyper-dimensional descriptor space.
BOX3D: Lightweight Camera-LiDAR Fusion for 3D Object Detection and Localization
Object detection and global localization play a crucial role in robotics, spanning across a great spectrum of applications from autonomous cars to multi-layered 3D Scene Graphs for semantic scene understanding.
Belief Scene Graphs: Expanding Partial Scenes with Objects through Computation of Expectation
As no database of 3D scene graphs exists for the training of the novel CECI model, we present a novel methodology for generating a 3D scene graph dataset based on semantically annotated real-life 3D spaces.
Event Camera and LiDAR based Human Tracking for Adverse Lighting Conditions in Subterranean Environments
In the proposed approach, information from the event camera and LiDAR are fused to localize a human or an object-of-interest in a robot's local frame.
NeBula: Quest for Robotic Autonomy in Challenging Environments; TEAM CoSTAR at the DARPA Subterranean Challenge
This paper presents and discusses algorithms, hardware, and software architecture developed by the TEAM CoSTAR (Collaborative SubTerranean Autonomous Robots), competing in the DARPA Subterranean Challenge.
A Subterranean Virtual Cave World for Gazebo based on the DARPA SubT Challenge
Subterranean environments with lots of obstacles, including narrow passages, large voids, rock falls and absence of illumination were always challenging for control, navigation, and perception of mobile robots.
Robotics
