[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-83897-en":3,"doc-seo-83897-105":28,"detail-sidebar-cat-0-en-105":90},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":11,"language":21,"language_code":22,"site_id":23,"html_lang":22,"table_of_contents":24,"faqs":25,"seo_title":13,"seo_description":14,"update_tm":26,"read_time":27},83897,8796095461610,"Oliver","https://ap-avatar.wpscdn.com/davatar_276721f389ce27ea32af1340a28f341c",8,"Research & Report","ECO Incremental Ego-Centric Octree Update for Point Streams","Constructing octrees for mobile robots that process continuous point streams in real time introduces major computational and memory bottlenecks, since standard global structures can incur high latency and unbalanced growth. The Ego-Centric Octree (ECO) provides a 3D sliding-window mapping space that dynamically bounds mapping to the robot’s immediate surroundings. ECO supports an incremental update that separates shift-out, shift-in, and overlap regions to avoid redundant global transforms. KITTI evaluations show up to 25.60% faster updates versus full reconstruction and up to 67.52% versus a bounded incremental baseline, while reducing downstream latency and maintaining short-term temporal memory in dynamic scenes.","ECO: Incremental Ego-Centric Octree Update for  \nPoint Streams  \nJaemin Yu†, Seongyoon Jeong†, Kang-Wook Chon∗ , and Duksu Kim∗ Korea University of Technology and Education (KOREATECH), Cheonan, Republic of Korea  \n†Equal contribution (co-first authors) ∗ Corresponding authors  \narXiv :2607 .05092v 1 [ cs .RO] 6 Jul 2026  \nAbstract—Constructing octrees for mobile robots that process continuous point streams in real time poses significant computational and memory challenges. Standard global structures often suffer from high latency and unbalanced tree growth. We introduce the Ego-Centric Octree (ECO), a spatial data structure that acts as a 3D sliding window, dynamically bounding the mapping space to the robot’s immediate surroundings. ECO uses an efficient incremental update algorithm that categorizes the environment into shift-out, shift-in, and overlap regions, eliminating redundant global coordinate transformations. Evaluationson the KITTI benchmark demonstrate that ECO reduces update times by up to 25.60% (24.87% on average) compared to full static reconstruction and by up to 67.52%(54.60% on average) compared to a bounded incremental baseline. Furthermore, ECO substantially lowers the total system latency of downstream tasks, running up to 34.17% faster than full reconstruction in voxelmap generation. In dynamic scenes, ECO naturally retains a short-term temporal memory of moving objects, providing useful temporal context while keeping update cost bounded and the tree balanced for real-time spatial perception.  \nIndex Terms—Mapping, Range Sensing, Incremental Octree, Ego-Centric Mapping, LiDAR.  \nI. INTRODUCTION  \nAn octree is a hierarchical data structure in which each internal node has exactly eight children, used to partition a three-dimensional space by recursively subdividing it into eight octants [1] . It serves as an approximate 3D space representation, offering a simple yet efficient method for managing spatial information. Due to these advantages, octrees are widely employed in various fields that handle 3D data, including simulation, robotics, and autonomous driving [2],[3],[4]. For instance, in mobile robotics, octrees are commonly used to build occupancy grid maps, which are crucial for tasks such as navigation and obstacle avoidance [5] .  \nDespite their utility, employing conventional octree construction approaches for mobile robots presents significant challenges. The primary issue is the high computational overhead associated with building an octree, especially when dealing with large-scale input data such as the dense point clouds generated by LiDAR sensors. While numerous studies have attempted to accelerate octree construction through methods such as parallel processing and hardware acceleration [6],[7], building an octree on-the-fly for real-time applications  \nThis work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessible.  \nsuch as autonomous driving remains a formidable task [8] . An alternative approach involves pre-processing, where an octree covering the entire target space is built beforehand [9] . However, this method suffers from high spatial overhead, demanding substantial memory resources. Furthermore, from the perspective of a mobile robot, defining a fixed target space is often impractical. A robot’s operational area can be vast and unpredictable, yet its immediate surroundings are typically the most critical region of interest. Since the robot’s future trajectory is unknown, pre-processing the octree for the path ahead is not a feasible strategy.  \nTo address these limitations, we tackle the problem of efficiently building and maintaining an octree for a mobile robot that perceives its environment as a continuous point stream. We introduce the Ego-Centric Octree (ECO), a novel concept in which the octree’s target space is dynamically bounded to a region of specific dimensions centered pre","cbCaivGbCUx0VTK1","https://ap.wps.com/l/cbCaivGbCUx0VTK1","pdf",997833,1,"English","en",105,"# Introduction\n# Related Work\n## Space-Partitioning Data Structures","[{\"question\":\"What problem does ECO address in real-time octree construction for mobile robots?\",\"answer\":\"ECO targets the computational and memory overhead of building and maintaining octrees from continuous LiDAR point streams, where global structures can cause high latency and unbalanced tree growth.\"},{\"question\":\"How does ECO define its mapping space?\",\"answer\":\"ECO dynamically bounds the octree’s target space as a 3D sliding window centered on the robot’s current position, focusing resources on the most relevant nearby region.\"},{\"question\":\"What performance improvements does ECO show on KITTI?\",\"answer\":\"ECO reduces update times by up to 25.60% (24.87% average) versus full static reconstruction and by up to 67.52% (54.60% average) versus a bounded incremental baseline, and it speeds voxel-map generation up to 34.17%.\"}]",1784191301,20,{"code":4,"msg":29,"data":30},"ok",{"site_id":23,"language":22,"slug":31,"title":13,"keywords":32,"description":14,"schema_data":33,"social_meta":85,"head_meta":87,"extra_data":89,"updated_unix":26},"eco-incremental-ego-centric-octree-update-for-point-streams","",{"@graph":34,"@context":84},[35,52,67],{"@type":36,"itemListElement":37},"BreadcrumbList",[38,42,46,49],{"item":39,"name":40,"@type":41,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":43,"name":44,"@type":41,"position":45},"https://docshare.wps.com/document/","Document",2,{"item":47,"name":12,"@type":41,"position":48},"https://docshare.wps.com/document/research-report/",3,{"item":50,"name":13,"@type":41,"position":51},"https://docshare.wps.com/document/eco-incremental-ego-centric-octree-update-for-point-streams/83897/",4,{"url":50,"name":13,"@type":53,"author":54,"headline":13,"publisher":56,"fileFormat":59,"inLanguage":22,"description":14,"dateModified":60,"datePublished":61,"encodingFormat":59,"isAccessibleForFree":62,"interactionStatistic":63},"DigitalDocument",{"name":9,"@type":55},"Person",{"url":39,"name":57,"@type":58},"DocShare","Organization","application/pdf","2026-07-17","2026-07-16",true,{"@type":64,"interactionType":65,"userInteractionCount":20},"InteractionCounter",{"@type":66},"ViewAction",{"@type":68,"mainEntity":69},"FAQPage",[70,76,80],{"name":71,"@type":72,"acceptedAnswer":73},"What problem does ECO address in real-time octree construction for mobile robots?","Question",{"text":74,"@type":75},"ECO targets the computational and memory overhead of building and maintaining octrees from continuous LiDAR point streams, where global structures can cause high latency and unbalanced tree growth.","Answer",{"name":77,"@type":72,"acceptedAnswer":78},"How does ECO define its mapping space?",{"text":79,"@type":75},"ECO dynamically bounds the octree’s target space as a 3D sliding window centered on the robot’s current position, focusing resources on the most relevant nearby region.",{"name":81,"@type":72,"acceptedAnswer":82},"What performance improvements does ECO show on KITTI?",{"text":83,"@type":75},"ECO reduces update times by up to 25.60% (24.87% average) versus full static reconstruction and by up to 67.52% (54.60% average) versus a bounded incremental baseline, and it speeds voxel-map generation up to 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