James J. Little

About

James J. Little is a prominent computer vision and robotics researcher whose work has fundamentally advanced how autonomous mobile robots perceive, navigate, and understand their environments. Best known for his pioneering contributions to vision-based simultaneous localization and mapping (SLAM), Little developed influential algorithms that enable robots to localize themselves and construct three-dimensional maps of unmodified, dynamic environments using scale-invariant visual features — work that has accumulated nearly 1,000 citations across his two landmark 2002 and 2005 papers alone. His early contributions to real-time stereo vision for robot navigation (408 citations) helped establish the viability of purely visual sensing as an alternative to laser-based systems. Little further refined probabilistic approaches to mapping through his application of Rao-Blackwellised particle filters to stereo vision SLAM, tackling challenging large-scale cyclic environments. Beyond navigation, his "Curious George" project (167 citations) demonstrated how attentive semantic reasoning could be integrated into robotic systems, bridging perception and higher-level understanding. With a body of work spanning global localization, autonomous exploration, and informed visual search, Little has made enduring contributions that continue to shape modern mobile robotics and computer vision research.

Research Focus

Key Achievements

Top Papers

1. 1
   Vision-based global localization and mapping for mobile robots

   481 citations · 2005
2. 2
   Vision-based mobile robot localization and mapping using scale-invariant features

   476 citations · 2002
3. 3
   Using Real-Time Stereo Vision for Mobile Robot Navigation

   408 citations · 2000
4. 4
   Global localization using distinctive visual features

   180 citations · 2003
5. 5
   Curious George: An attentive semantic robot

   167 citations · 2008
6. 6
   Local and global localization for mobile robots using visual landmarks

   144 citations · 2002
7. 7
   σSLAM: stereo vision SLAM using the Rao-Blackwellised particle filter and a novel mixture proposal distribution

   109 citations · 2006
8. 8
   Autonomous vision-based exploration and mapping using hybrid maps and Rao-Blackwellised particle filters

   102 citations · 2006
9. 9
   A Study of the Rao-Blackwellised Particle Filter for Efficient and Accurate Vision-Based SLAM

   97 citations · 2007
10. 10
    Informed visual search: Combining attention and object recognition

    70 citations · 2008

Key Collaborators

Contact & Links