6 citations · 20 across the 7 of their papers we have counts for
9 papers · 1 filter
A Review on Visual-SLAM: Advancements from Geometric Modelling to Learning-based Semantic Scene Understanding
Tin Lai
Simultaneous Localisation and Mapping (SLAM) is one of the fundamental problems in autonomous mobile robots where a robot needs to reconstruct a previously unseen environment while…
sbp-env: Sampling-based Motion Planners' Testing Environment
Tin Lai
Sampling-based motion planners' testing environment (sbp-env) is a full feature framework to quickly test different sampling-based algorithms for motion planning. sbp-env focuses o…
Parallelised Diffeomorphic Sampling-based Motion Planning
Tin Lai, Weiming Zhi, Tucker Hermans +1
We propose Parallelised Diffeomorphic Sampling-based Motion Planning (PDMP). PDMP is a novel parallelised framework that uses bijective and differentiable mappings, or diffeomorphi…
Rapidly-exploring Random Forest: Adaptively Exploits Local Structure with Generalised Multi-Trees Motion Planning
Tin Lai
Sampling-based motion planners perform exceptionally well in robotic applications that operate in high-dimensional space. However, most works often constrain the planning workspace…
Anticipatory Navigation in Crowds by Probabilistic Prediction of Pedestrian Future Movements
Weiming Zhi, Tin Lai, Lionel Ott +1
Critical for the coexistence of humans and robots in dynamic environments is the capability for agents to understand each other's actions, and anticipate their movements. This pape…
Learning to Plan Optimally with Flow-based Motion Planner
Tin Lai, Fabio Ramos
Sampling-based motion planning is the predominant paradigm in many real-world robotic applications, but its performance is immensely dependent on the quality of the samples. The ma…